Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface

PubMed Central

Wang, Libo; Li, Lianlin; Li, Yunbo; Zhang, Hao Chi; Cui, Tie Jun

2016-01-01

Real-time high-resolution (including super-resolution) imaging with low-cost hardware is a long sought-after goal in various imaging applications. Here, we propose broadband single-shot and single-sensor high-/super-resolution imaging by using a spatio-temporal dispersive metasurface and an imaging reconstruction algorithm. The metasurface with spatio-temporal dispersive property ensures the feasibility of the single-shot and single-sensor imager for super- and high-resolution imaging, since it can convert efficiently the detailed spatial information of the probed object into one-dimensional time- or frequency-dependent signal acquired by a single sensor fixed in the far-field region. The imaging quality can be improved by applying a feature-enhanced reconstruction algorithm in post-processing, and the desired imaging resolution is related to the distance between the object and metasurface. When the object is placed in the vicinity of the metasurface, the super-resolution imaging can be realized. The proposed imaging methodology provides a unique means to perform real-time data acquisition, high-/super-resolution images without employing expensive hardware (e.g. mechanical scanner, antenna array, etc.). We expect that this methodology could make potential breakthroughs in the areas of microwave, terahertz, optical, and even ultrasound imaging. PMID:27246668

Simultaneous digital super-resolution and nonuniformity correction for infrared imaging systems.

PubMed

Meza, Pablo; Machuca, Guillermo; Torres, Sergio; Martin, Cesar San; Vera, Esteban

2015-07-20

In this article, we present a novel algorithm to achieve simultaneous digital super-resolution and nonuniformity correction from a sequence of infrared images. We propose to use spatial regularization terms that exploit nonlocal means and the absence of spatial correlation between the scene and the nonuniformity noise sources. We derive an iterative optimization algorithm based on a gradient descent minimization strategy. Results from infrared image sequences corrupted with simulated and real fixed-pattern noise show a competitive performance compared with state-of-the-art methods. A qualitative analysis on the experimental results obtained with images from a variety of infrared cameras indicates that the proposed method provides super-resolution images with significantly less fixed-pattern noise.

Single-image super-resolution based on Markov random field and contourlet transform

NASA Astrophysics Data System (ADS)

Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

2011-04-01

Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization.

PubMed

Li, Yiming; Ishitsuka, Yuji; Hedde, Per Niklas; Nienhaus, G Ulrich

2013-06-25

In localization-based super-resolution microscopy, individual fluorescent markers are stochastically photoactivated and subsequently localized within a series of camera frames, yielding a final image with a resolution far beyond the diffraction limit. Yet, before localization can be performed, the subregions within the frames where the individual molecules are present have to be identified-oftentimes in the presence of high background. In this work, we address the importance of reliable molecule identification for the quality of the final reconstructed super-resolution image. We present a fast and robust algorithm (a-livePALM) that vastly improves the molecule detection efficiency while minimizing false assignments that can lead to image artifacts.

Wavelength scanning achieves pixel super-resolution in holographic on-chip microscopy

NASA Astrophysics Data System (ADS)

Luo, Wei; Göröcs, Zoltan; Zhang, Yibo; Feizi, Alborz; Greenbaum, Alon; Ozcan, Aydogan

2016-03-01

Lensfree holographic on-chip imaging is a potent solution for high-resolution and field-portable bright-field imaging over a wide field-of-view. Previous lensfree imaging approaches utilize a pixel super-resolution technique, which relies on sub-pixel lateral displacements between the lensfree diffraction patterns and the image sensor's pixel-array, to achieve sub-micron resolution under unit magnification using state-of-the-art CMOS imager chips, commonly used in e.g., mobile-phones. Here we report, for the first time, a wavelength scanning based pixel super-resolution technique in lensfree holographic imaging. We developed an iterative super-resolution algorithm, which generates high-resolution reconstructions of the specimen from low-resolution (i.e., under-sampled) diffraction patterns recorded at multiple wavelengths within a narrow spectral range (e.g., 10-30 nm). Compared with lateral shift-based pixel super-resolution, this wavelength scanning approach does not require any physical shifts in the imaging setup, and the resolution improvement is uniform in all directions across the sensor-array. Our wavelength scanning super-resolution approach can also be integrated with multi-height and/or multi-angle on-chip imaging techniques to obtain even higher resolution reconstructions. For example, using wavelength scanning together with multi-angle illumination, we achieved a halfpitch resolution of 250 nm, corresponding to a numerical aperture of 1. In addition to pixel super-resolution, the small scanning steps in wavelength also enable us to robustly unwrap phase, revealing the specimen's optical path length in our reconstructed images. We believe that this new wavelength scanning based pixel super-resolution approach can provide competitive microscopy solutions for high-resolution and field-portable imaging needs, potentially impacting tele-pathology applications in resource-limited-settings.

Texton-based super-resolution for achieving high spatiotemporal resolution in hybrid camera system

NASA Astrophysics Data System (ADS)

Kamimura, Kenji; Tsumura, Norimichi; Nakaguchi, Toshiya; Miyake, Yoichi

2010-05-01

Many super-resolution methods have been proposed to enhance the spatial resolution of images by using iteration and multiple input images. In a previous paper, we proposed the example-based super-resolution method to enhance an image through pixel-based texton substitution to reduce the computational cost. In this method, however, we only considered the enhancement of a texture image. In this study, we modified this texton substitution method for a hybrid camera to reduce the required bandwidth of a high-resolution video camera. We applied our algorithm to pairs of high- and low-spatiotemporal-resolution videos, which were synthesized to simulate a hybrid camera. The result showed that the fine detail of the low-resolution video can be reproduced compared with bicubic interpolation and the required bandwidth could be reduced to about 1/5 in a video camera. It was also shown that the peak signal-to-noise ratios (PSNRs) of the images improved by about 6 dB in a trained frame and by 1.0-1.5 dB in a test frame, as determined by comparison with the processed image using bicubic interpolation, and the average PSNRs were higher than those obtained by the well-known Freeman’s patch-based super-resolution method. Compared with that of the Freeman’s patch-based super-resolution method, the computational time of our method was reduced to almost 1/10.

A Super-Resolution Algorithm for Enhancement of FLASH LIDAR Data: Flight Test Results

NASA Technical Reports Server (NTRS)

Bulyshev, Alexander; Amzajerdian, Farzin; Roback, Eric; Reisse Robert

2014-01-01

This paper describes the results of a 3D super-resolution algorithm applied to the range data obtained from a recent Flash Lidar helicopter flight test. The flight test was conducted by the NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) project over a simulated lunar terrain facility at NASA Kennedy Space Center. ALHAT is developing the technology for safe autonomous landing on the surface of celestial bodies: Moon, Mars, asteroids. One of the test objectives was to verify the ability of 3D super-resolution technique to generate high resolution digital elevation models (DEMs) and to determine time resolved relative positions and orientations of the vehicle. 3D super-resolution algorithm was developed earlier and tested in computational modeling, and laboratory experiments, and in a few dynamic experiments using a moving truck. Prior to the helicopter flight test campaign, a 100mX100m hazard field was constructed having most of the relevant extraterrestrial hazard: slopes, rocks, and craters with different sizes. Data were collected during the flight and then processed by the super-resolution code. The detailed DEM of the hazard field was constructed using independent measurement to be used for comparison. ALHAT navigation system data were used to verify abilities of super-resolution method to provide accurate relative navigation information. Namely, the 6 degree of freedom state vector of the instrument as a function of time was restored from super-resolution data. The results of comparisons show that the super-resolution method can construct high quality DEMs and allows for identifying hazards like rocks and craters within the accordance of ALHAT requirements.

A super-resolution algorithm for enhancement of flash lidar data: flight test results

NASA Astrophysics Data System (ADS)

Bulyshev, Alexander; Amzajerdian, Farzin; Roback, Eric; Reisse, Robert

2013-03-01

This paper describes the results of a 3D super-resolution algorithm applied to the range data obtained from a recent Flash Lidar helicopter flight test. The flight test was conducted by the NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) project over a simulated lunar terrain facility at NASA Kennedy Space Center. ALHAT is developing the technology for safe autonomous landing on the surface of celestial bodies: Moon, Mars, asteroids. One of the test objectives was to verify the ability of 3D super-resolution technique to generate high resolution digital elevation models (DEMs) and to determine time resolved relative positions and orientations of the vehicle. 3D super-resolution algorithm was developed earlier and tested in computational modeling, and laboratory experiments, and in a few dynamic experiments using a moving truck. Prior to the helicopter flight test campaign, a 100mX100m hazard field was constructed having most of the relevant extraterrestrial hazard: slopes, rocks, and craters with different sizes. Data were collected during the flight and then processed by the super-resolution code. The detailed DEM of the hazard field was constructed using independent measurement to be used for comparison. ALHAT navigation system data were used to verify abilities of super-resolution method to provide accurate relative navigation information. Namely, the 6 degree of freedom state vector of the instrument as a function of time was restored from super-resolution data. The results of comparisons show that the super-resolution method can construct high quality DEMs and allows for identifying hazards like rocks and craters within the accordance of ALHAT requirements.

Cygnus A super-resolved via convex optimization from VLA data

NASA Astrophysics Data System (ADS)

Dabbech, A.; Onose, A.; Abdulaziz, A.; Perley, R. A.; Smirnov, O. M.; Wiaux, Y.

2018-05-01

We leverage the Sparsity Averaging Re-weighted Analysis approach for interferometric imaging, that is based on convex optimization, for the super-resolution of Cyg A from observations at the frequencies 8.422 and 6.678 GHz with the Karl G. Jansky Very Large Array (VLA). The associated average sparsity and positivity priors enable image reconstruction beyond instrumental resolution. An adaptive Preconditioned primal-dual algorithmic structure is developed for imaging in the presence of unknown noise levels and calibration errors. We demonstrate the superior performance of the algorithm with respect to the conventional CLEAN-based methods, reflected in super-resolved images with high fidelity. The high-resolution features of the recovered images are validated by referring to maps of Cyg A at higher frequencies, more precisely 17.324 and 14.252 GHz. We also confirm the recent discovery of a radio transient in Cyg A, revealed in the recovered images of the investigated data sets. Our MATLAB code is available online on GitHub.

Light-sheet Bayesian microscopy enables deep-cell super-resolution imaging of heterochromatin in live human embryonic stem cells.

PubMed

Hu, Ying S; Zhu, Quan; Elkins, Keri; Tse, Kevin; Li, Yu; Fitzpatrick, James A J; Verma, Inder M; Cang, Hu

2013-01-01

Heterochromatin in the nucleus of human embryonic cells plays an important role in the epigenetic regulation of gene expression. The architecture of heterochromatin and its dynamic organization remain elusive because of the lack of fast and high-resolution deep-cell imaging tools. We enable this task by advancing instrumental and algorithmic implementation of the localization-based super-resolution technique. We present light-sheet Bayesian super-resolution microscopy (LSBM). We adapt light-sheet illumination for super-resolution imaging by using a novel prism-coupled condenser design to illuminate a thin slice of the nucleus with high signal-to-noise ratio. Coupled with a Bayesian algorithm that resolves overlapping fluorophores from high-density areas, we show, for the first time, nanoscopic features of the heterochromatin structure in both fixed and live human embryonic stem cells. The enhanced temporal resolution allows capturing the dynamic change of heterochromatin with a lateral resolution of 50-60 nm on a time scale of 2.3 s. Light-sheet Bayesian microscopy opens up broad new possibilities of probing nanometer-scale nuclear structures and real-time sub-cellular processes and other previously difficult-to-access intracellular regions of living cells at the single-molecule, and single cell level.

Light-sheet Bayesian microscopy enables deep-cell super-resolution imaging of heterochromatin in live human embryonic stem cells

PubMed Central

Hu, Ying S; Zhu, Quan; Elkins, Keri; Tse, Kevin; Li, Yu; Fitzpatrick, James A J; Verma, Inder M; Cang, Hu

2016-01-01

Background Heterochromatin in the nucleus of human embryonic cells plays an important role in the epigenetic regulation of gene expression. The architecture of heterochromatin and its dynamic organization remain elusive because of the lack of fast and high-resolution deep-cell imaging tools. We enable this task by advancing instrumental and algorithmic implementation of the localization-based super-resolution technique. Results We present light-sheet Bayesian super-resolution microscopy (LSBM). We adapt light-sheet illumination for super-resolution imaging by using a novel prism-coupled condenser design to illuminate a thin slice of the nucleus with high signal-to-noise ratio. Coupled with a Bayesian algorithm that resolves overlapping fluorophores from high-density areas, we show, for the first time, nanoscopic features of the heterochromatin structure in both fixed and live human embryonic stem cells. The enhanced temporal resolution allows capturing the dynamic change of heterochromatin with a lateral resolution of 50–60 nm on a time scale of 2.3 s. Conclusion Light-sheet Bayesian microscopy opens up broad new possibilities of probing nanometer-scale nuclear structures and real-time sub-cellular processes and other previously difficult-to-access intracellular regions of living cells at the single-molecule, and single cell level. PMID:27795878

Enhancing Deep-Water Low-Resolution Gridded Bathymetry Using Single Image Super-Resolution

NASA Astrophysics Data System (ADS)

Elmore, P. A.; Nock, K.; Bonanno, D.; Smith, L.; Ferrini, V. L.; Petry, F. E.

2017-12-01

We present research to employ single-image super-resolution (SISR) algorithms to enhance knowledge of the seafloor using the 1-minute GEBCO 2014 grid when 100m grids from high-resolution sonar systems are available for training. Our numerical upscaling experiments of x15 upscaling of the GEBCO grid along three areas of the Eastern Pacific Ocean along mid-ocean ridge systems where we have these 100m gridded bathymetry data sets, which we accept as ground-truth. We show that four SISR algorithms can enhance this low-resolution knowledge of bathymetry versus bicubic or Spline-In-Tension algorithms through upscaling under these conditions: 1) rough topography is present in both training and testing areas and 2) the range of depths and features in the training area contains the range of depths in the enhancement area. We quantitatively judged successful SISR enhancement versus bicubic interpolation when Student's hypothesis testing show significant improvement of the root-mean squared error (RMSE) between upscaled bathymetry and 100m gridded ground-truth bathymetry at p < 0.05. In addition, we found evidence that random forest based SISR methods may provide more robust enhancements versus non-forest based SISR algorithms.

Multiple signal classification algorithm for super-resolution fluorescence microscopy

PubMed Central

Agarwal, Krishna; Macháň, Radek

2016-01-01

Single-molecule localization techniques are restricted by long acquisition and computational times, or the need of special fluorophores or biologically toxic photochemical environments. Here we propose a statistical super-resolution technique of wide-field fluorescence microscopy we call the multiple signal classification algorithm which has several advantages. It provides resolution down to at least 50 nm, requires fewer frames and lower excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the timescale of the recording. The multiple signal classification algorithm shows comparable or better performance in comparison with single-molecule localization techniques and four contemporary statistical super-resolution methods for experiments of in vitro actin filaments and other independently acquired experimental data sets. We also demonstrate super-resolution at timescales of 245 ms (using 49 frames acquired at 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments imaged without imaging buffers. PMID:27934858

Single image super-resolution reconstruction algorithm based on eage selection

NASA Astrophysics Data System (ADS)

Zhang, Yaolan; Liu, Yijun

2017-05-01

Super-resolution (SR) has become more important, because it can generate high-quality high-resolution (HR) images from low-resolution (LR) input images. At present, there are a lot of work is concentrated on developing sophisticated image priors to improve the image quality, while taking much less attention to estimating and incorporating the blur model that can also impact the reconstruction results. We present a new reconstruction method based on eager selection. This method takes full account of the factors that affect the blur kernel estimation and accurately estimating the blur process. When comparing with the state-of-the-art methods, our method has comparable performance.

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-02-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-05-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Windowed time-reversal music technique for super-resolution ultrasound imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lianjie; Labyed, Yassin

Systems and methods for super-resolution ultrasound imaging using a windowed and generalized TR-MUSIC algorithm that divides the imaging region into overlapping sub-regions and applies the TR-MUSIC algorithm to the windowed backscattered ultrasound signals corresponding to each sub-region. The algorithm is also structured to account for the ultrasound attenuation in the medium and the finite-size effects of ultrasound transducer elements.

Super Resolution and Interference Suppression Technique applied to SHARAD Radar Data

NASA Astrophysics Data System (ADS)

Raguso, M. C.; Mastrogiuseppe, M.; Seu, R.; Piazzo, L.

2017-12-01

We will present a super resolution and interference suppression technique applied to the data acquired by the SHAllow RADar (SHARAD) on board the NASA's 2005 Mars Reconnaissance Orbiter (MRO) mission, currently operating around Mars [1]. The algorithms allow to improve the range resolution roughly by a factor of 3 and the Signal to Noise Ratio (SNR) by a several decibels. Range compression algorithms usually adopt conventional Fourier transform techniques, which are limited in the resolution by the transmitted signal bandwidth, analogous to the Rayleigh's criterion in optics. In this work, we investigate a super resolution method based on autoregressive models and linear prediction techniques [2]. Starting from the estimation of the linear prediction coefficients from the spectral data, the algorithm performs the radar bandwidth extrapolation (BWE), thereby improving the range resolution of the pulse-compressed coherent radar data. Moreover, the EMIs (ElectroMagnetic Interferences) are detected and the spectra is interpolated in order to reconstruct an interference free spectrum, thereby improving the SNR. The algorithm can be applied to the single complex look image after synthetic aperture processing (SAR). We apply the proposed algorithm to simulated as well as to real radar data. We will demonstrate the effective enhancement on vertical resolution with respect to the classical spectral estimator. We will show that the imaging of the subsurface layered structures observed in radargrams is improved, allowing additional insights for the scientific community in the interpretation of the SHARAD radar data, which will help to further our understanding of the formation and evolution of known geological features on Mars. References: [1] Seu et al. 2007, Science, 2007, 317, 1715-1718 [2] K.M. Cuomo, "A Bandwidth Extrapolation Technique for Improved Range Resolution of Coherent Radar Data", Project Report CJP-60, Revision 1, MIT Lincoln Laboratory (4 Dec. 1992).

Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters

NASA Astrophysics Data System (ADS)

Sargent, Garrett C.; Ratliff, Bradley M.; Asari, Vijayan K.

2017-08-01

The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

TestSTORM: Simulator for optimizing sample labeling and image acquisition in localization based super-resolution microscopy

PubMed Central

Sinkó, József; Kákonyi, Róbert; Rees, Eric; Metcalf, Daniel; Knight, Alex E.; Kaminski, Clemens F.; Szabó, Gábor; Erdélyi, Miklós

2014-01-01

Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments. PMID:24688813

Image super-resolution via sparse representation.

PubMed

Yang, Jianchao; Wright, John; Huang, Thomas S; Ma, Yi

2010-11-01

This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.

A Bayesian Nonparametric Approach to Image Super-Resolution.

PubMed

Polatkan, Gungor; Zhou, Mingyuan; Carin, Lawrence; Blei, David; Daubechies, Ingrid

2015-02-01

Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric, the number of elements found is also determined from the data. We test the results on both benchmark and natural images, comparing with several other models from the research literature. We perform large-scale human evaluation experiments to assess the visual quality of the results. In a first implementation, we use Gibbs sampling to approximate the posterior. However, this algorithm is not feasible for large-scale data. To circumvent this, we then develop an online variational Bayes (VB) algorithm. This algorithm finds high quality dictionaries in a fraction of the time needed by the Gibbs sampler.

High Resolution Bathymetry Estimation Improvement with Single Image Super-Resolution Algorithm Super-Resolution Forests

DTIC Science & Technology

2017-01-26

Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/5514--17-9692 High Resolution Bathymetry Estimation Improvement with Single Image Super...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate

Photon-efficient super-resolution laser radar

NASA Astrophysics Data System (ADS)

Shin, Dongeek; Shapiro, Jeffrey H.; Goyal, Vivek K.

2017-08-01

The resolution achieved in photon-efficient active optical range imaging systems can be low due to non-idealities such as propagation through a diffuse scattering medium. We propose a constrained optimization-based frame- work to address extremes in scarcity of photons and blurring by a forward imaging kernel. We provide two algorithms for the resulting inverse problem: a greedy algorithm, inspired by sparse pursuit algorithms; and a convex optimization heuristic that incorporates image total variation regularization. We demonstrate that our framework outperforms existing deconvolution imaging techniques in terms of peak signal-to-noise ratio. Since our proposed method is able to super-resolve depth features using small numbers of photon counts, it can be useful for observing fine-scale phenomena in remote sensing through a scattering medium and through-the-skin biomedical imaging applications.

Adaptive Wiener filter super-resolution of color filter array images.

PubMed

Karch, Barry K; Hardie, Russell C

2013-08-12

Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.

Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

NASA Astrophysics Data System (ADS)

Ying, Changsheng; Zhao, Peng; Li, Ye

2018-01-01

The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.

Sparse super-resolution reconstructions of video from mobile devices in digital TV broadcast applications

NASA Astrophysics Data System (ADS)

Boon, Choong S.; Guleryuz, Onur G.; Kawahara, Toshiro; Suzuki, Yoshinori

2006-08-01

We consider the mobile service scenario where video programming is broadcast to low-resolution wireless terminals. In such a scenario, broadcasters utilize simultaneous data services and bi-directional communications capabilities of the terminals in order to offer substantially enriched viewing experiences to users by allowing user participation and user tuned content. While users immediately benefit from this service when using their phones in mobile environments, the service is less appealing in stationary environments where a regular television provides competing programming at much higher display resolutions. We propose a fast super-resolution technique that allows the mobile terminals to show a much enhanced version of the broadcast video on nearby high-resolution devices, extending the appeal and usefulness of the broadcast service. The proposed single frame super-resolution algorithm uses recent sparse recovery results to provide high quality and high-resolution video reconstructions based solely on individual decoded frames provided by the low-resolution broadcast.

Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution

PubMed Central

Bishara, Waheb; Su, Ting-Wei; Coskun, Ahmet F.; Ozcan, Aydogan

2010-01-01

We demonstrate lensfree holographic microscopy on a chip to achieve ~0.6 µm spatial resolution corresponding to a numerical aperture of ~0.5 over a large field-of-view of ~24 mm2. By using partially coherent illumination from a large aperture (~50 µm), we acquire lower resolution lensfree in-line holograms of the objects with unit fringe magnification. For each lensfree hologram, the pixel size at the sensor chip limits the spatial resolution of the reconstructed image. To circumvent this limitation, we implement a sub-pixel shifting based super-resolution algorithm to effectively recover much higher resolution digital holograms of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. We demonstrate the success of this pixel super-resolution approach by imaging patterned transparent substrates, blood smear samples, as well as Caenoharbditis Elegans. PMID:20588977

Super-resolution for imagery from integrated microgrid polarimeters.

PubMed

Hardie, Russell C; LeMaster, Daniel A; Ratliff, Bradley M

2011-07-04

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

Time reversal and phase coherent music techniques for super-resolution ultrasound imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lianjie; Labyed, Yassin

Systems and methods for super-resolution ultrasound imaging using a windowed and generalized TR-MUSIC algorithm that divides the imaging region into overlapping sub-regions and applies the TR-MUSIC algorithm to the windowed backscattered ultrasound signals corresponding to each sub-region. The algorithm is also structured to account for the ultrasound attenuation in the medium and the finite-size effects of ultrasound transducer elements. A modified TR-MUSIC imaging algorithm is used to account for ultrasound scattering from both density and compressibility contrasts. The phase response of ultrasound transducer elements is accounted for in a PC-MUSIC system.

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

PubMed Central

Hosny, Neveen A.; Song, Mingying; Connelly, John T.; Ameer-Beg, Simon; Knight, Martin M.; Wheeler, Ann P.

2013-01-01

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging. PMID:24130668

Portable and cost-effective pixel super-resolution on-chip microscope for telemedicine applications.

PubMed

Bishara, Waheb; Sikora, Uzair; Mudanyali, Onur; Su, Ting-Wei; Yaglidere, Oguzhan; Luckhart, Shirley; Ozcan, Aydogan

2011-01-01

We report a field-portable lensless on-chip microscope with a lateral resolution of <1 μm and a large field-of-view of ~24 mm(2). This microscope is based on digital in-line holography and a pixel super-resolution algorithm to process multiple lensfree holograms and obtain a single high-resolution hologram. In its compact and cost-effective design, we utilize 23 light emitting diodes butt-coupled to 23 multi-mode optical fibers, and a simple optical filter, with no moving parts. Weighing only ~95 grams, we demonstrate the performance of this field-portable microscope by imaging various objects including human malaria parasites in thin blood smears.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

PubMed Central

Fan, Chong; Wu, Chaoyun; Li, Grand; Ma, Jun

2017-01-01

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. PMID:28208837

Introduction to the virtual special issue on super-resolution imaging techniques

NASA Astrophysics Data System (ADS)

Cao, Liangcai; Liu, Zhengjun

2017-12-01

Until quite recently, the resolution of optical imaging instruments, including telescopes, cameras and microscopes, was considered to be limited by the diffraction of light and by image sensors. In the past few years, many exciting super-resolution approaches have emerged that demonstrate intriguing ways to bypass the classical limit in optics and detectors. More and more research groups are engaged in the study of advanced super-resolution schemes, devices, algorithms, systems, and applications [1-6]. Super-resolution techniques involve new methods in science and engineering of optics [7,8], measurements [9,10], chemistry [11,12] and information [13,14]. Promising applications, particularly in biomedical research and semiconductor industry, have been successfully demonstrated.

Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors.

PubMed

Lukeš, Tomáš; Pospíšil, Jakub; Fliegel, Karel; Lasser, Theo; Hagen, Guy M

2018-03-01

Super-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared with organic dyes, which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms. Four complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented, including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes. This dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy.

PubMed

Huang, Xiaoshuai; Fan, Junchao; Li, Liuju; Liu, Haosen; Wu, Runlong; Wu, Yi; Wei, Lisi; Mao, Heng; Lal, Amit; Xi, Peng; Tang, Liqiang; Zhang, Yunfeng; Liu, Yanmei; Tan, Shan; Chen, Liangyi

2018-06-01

To increase the temporal resolution and maximal imaging time of super-resolution (SR) microscopy, we have developed a deconvolution algorithm for structured illumination microscopy based on Hessian matrixes (Hessian-SIM). It uses the continuity of biological structures in multiple dimensions as a priori knowledge to guide image reconstruction and attains artifact-minimized SR images with less than 10% of the photon dose used by conventional SIM while substantially outperforming current algorithms at low signal intensities. Hessian-SIM enables rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts and with a spatiotemporal resolution of 88 nm and 188 Hz. Its high sensitivity allows the use of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins, enabling hour-long time-lapse SR imaging of actin filaments in live cells. Finally, we observed the structural dynamics of mitochondrial cristae and structures that, to our knowledge, have not been observed previously, such as enlarged fusion pores during vesicle exocytosis.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

PubMed Central

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-01-01

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. PMID:26007744

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

PubMed

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Single-Image Super-Resolution Based on Rational Fractal Interpolation.

PubMed

Zhang, Yunfeng; Fan, Qinglan; Bao, Fangxun; Liu, Yifang; Zhang, Caiming

2018-08-01

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

Fast super-resolution with affine motion using an adaptive Wiener filter and its application to airborne imaging.

PubMed

Hardie, Russell C; Barnard, Kenneth J; Ordonez, Raul

2011-12-19

Fast nonuniform interpolation based super-resolution (SR) has traditionally been limited to applications with translational interframe motion. This is in part because such methods are based on an underlying assumption that the warping and blurring components in the observation model commute. For translational motion this is the case, but it is not true in general. This presents a problem for applications such as airborne imaging where translation may be insufficient. Here we present a new Fourier domain analysis to show that, for many image systems, an affine warping model with limited zoom and shear approximately commutes with the point spread function when diffraction effects are modeled. Based on this important result, we present a new fast adaptive Wiener filter (AWF) SR algorithm for non-translational motion and study its performance with affine motion. The fast AWF SR method employs a new smart observation window that allows us to precompute all the needed filter weights for any type of motion without sacrificing much of the full performance of the AWF. We evaluate the proposed algorithm using simulated data and real infrared airborne imagery that contains a thermal resolution target allowing for objective resolution analysis.

Field-Portable Pixel Super-Resolution Colour Microscope

PubMed Central

Greenbaum, Alon; Akbari, Najva; Feizi, Alborz; Luo, Wei; Ozcan, Aydogan

2013-01-01

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm2. This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate ‘rainbow’ like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings. PMID:24086742

Field-portable pixel super-resolution colour microscope.

PubMed

Greenbaum, Alon; Akbari, Najva; Feizi, Alborz; Luo, Wei; Ozcan, Aydogan

2013-01-01

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm(2). This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate 'rainbow' like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings.

Resolution enhancement of tri-stereo remote sensing images by super resolution methods

NASA Astrophysics Data System (ADS)

Tuna, Caglayan; Akoguz, Alper; Unal, Gozde; Sertel, Elif

2016-10-01

Super resolution (SR) refers to generation of a High Resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single frame or multi-frame that contains a collection of several images acquired from slightly different views of the same observation area. In this study, we propose a novel application of tri-stereo Remote Sensing (RS) satellite images to the super resolution problem. Since the tri-stereo RS images of the same observation area are acquired from three different viewing angles along the flight path of the satellite, these RS images are properly suited to a SR application. We first estimate registration between the chosen reference LR image and other LR images to calculate the sub pixel shifts among the LR images. Then, the warping, blurring and down sampling matrix operators are created as sparse matrices to avoid high memory and computational requirements, which would otherwise make the RS-SR solution impractical. Finally, the overall system matrix, which is constructed based on the obtained operator matrices is used to obtain the estimate HR image in one step in each iteration of the SR algorithm. Both the Laplacian and total variation regularizers are incorporated separately into our algorithm and the results are presented to demonstrate an improved quantitative performance against the standard interpolation method as well as improved qualitative results due expert evaluations.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Comparison between beamforming and super resolution imaging algorithms for non-destructive evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Chengguang; Drinkwater, Bruce W.

In this paper the performance of total focusing method is compared with the widely used time-reversal MUSIC super resolution technique. The algorithms are tested with simulated and experimental ultrasonic array data, each containing different noise levels. The simulated time domain signals allow the effects of array geometry, frequency, scatterer location, scatterer size, scatterer separation and random noise to be carefully controlled. The performance of the imaging algorithms is evaluated in terms of resolution and sensitivity to random noise. It is shown that for the low noise situation, time-reversal MUSIC provides enhanced lateral resolution when compared to the total focusing method.more » However, for higher noise levels, the total focusing method shows robustness, whilst the performance of time-reversal MUSIC is significantly degraded.« less

Multiple-image hiding using super resolution reconstruction in high-frequency domains

NASA Astrophysics Data System (ADS)

Li, Xiao-Wei; Zhao, Wu-Xiang; Wang, Jun; Wang, Qiong-Hua

2017-12-01

In this paper, a robust multiple-image hiding method using the computer-generated integral imaging and the modified super-resolution reconstruction algorithm is proposed. In our work, the host image is first transformed into frequency domains by cellular automata (CA), to assure the quality of the stego-image, the secret images are embedded into the CA high-frequency domains. The proposed method has the following advantages: (1) robustness to geometric attacks because of the memory-distributed property of elemental images, (2) increasing quality of the reconstructed secret images as the scheme utilizes the modified super-resolution reconstruction algorithm. The simulation results show that the proposed multiple-image hiding method outperforms other similar hiding methods and is robust to some geometric attacks, e.g., Gaussian noise and JPEG compression attacks.

Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

NASA Astrophysics Data System (ADS)

Mao, Deqing; Zhang, Yin; Zhang, Yongchao; Huang, Yulin; Yang, Jianyu

2018-01-01

Doppler beam sharpening (DBS) is a critical technology for airborne radar ground mapping in forward-squint region. In conventional DBS technology, the narrow-band Doppler filter groups formed by fast Fourier transform (FFT) method suffer from low spectral resolution and high side lobe levels. The iterative adaptive approach (IAA), based on the weighted least squares (WLS), is applied to the DBS imaging applications, forming narrower Doppler filter groups than the FFT with lower side lobe levels. Regrettably, the IAA is iterative, and requires matrix multiplication and inverse operation when forming the covariance matrix, its inverse and traversing the WLS estimate for each sampling point, resulting in a notably high computational complexity for cubic time. We propose a fast IAA (FIAA)-based super-resolution DBS imaging method, taking advantage of the rich matrix structures of the classical narrow-band filtering. First, we formulate the covariance matrix via the FFT instead of the conventional matrix multiplication operation, based on the typical Fourier structure of the steering matrix. Then, by exploiting the Gohberg-Semencul representation, the inverse of the Toeplitz covariance matrix is computed by the celebrated Levinson-Durbin (LD) and Toeplitz-vector algorithm. Finally, the FFT and fast Toeplitz-vector algorithm are further used to traverse the WLS estimates based on the data-dependent trigonometric polynomials. The method uses the Hermitian feature of the echo autocorrelation matrix R to achieve its fast solution and uses the Toeplitz structure of R to realize its fast inversion. The proposed method enjoys a lower computational complexity without performance loss compared with the conventional IAA-based super-resolution DBS imaging method. The results based on simulations and measured data verify the imaging performance and the operational efficiency.

Super-Resolution in Plenoptic Cameras Using FPGAs

PubMed Central

Pérez, Joel; Magdaleno, Eduardo; Pérez, Fernando; Rodríguez, Manuel; Hernández, David; Corrales, Jaime

2014-01-01

Plenoptic cameras are a new type of sensor that extend the possibilities of current commercial cameras allowing 3D refocusing or the capture of 3D depths. One of the limitations of plenoptic cameras is their limited spatial resolution. In this paper we describe a fast, specialized hardware implementation of a super-resolution algorithm for plenoptic cameras. The algorithm has been designed for field programmable graphic array (FPGA) devices using VHDL (very high speed integrated circuit (VHSIC) hardware description language). With this technology, we obtain an acceleration of several orders of magnitude using its extremely high-performance signal processing capability through parallelism and pipeline architecture. The system has been developed using generics of the VHDL language. This allows a very versatile and parameterizable system. The system user can easily modify parameters such as data width, number of microlenses of the plenoptic camera, their size and shape, and the super-resolution factor. The speed of the algorithm in FPGA has been successfully compared with the execution using a conventional computer for several image sizes and different 3D refocusing planes. PMID:24841246

Super-resolution in plenoptic cameras using FPGAs.

PubMed

Pérez, Joel; Magdaleno, Eduardo; Pérez, Fernando; Rodríguez, Manuel; Hernández, David; Corrales, Jaime

2014-05-16

Plenoptic cameras are a new type of sensor that extend the possibilities of current commercial cameras allowing 3D refocusing or the capture of 3D depths. One of the limitations of plenoptic cameras is their limited spatial resolution. In this paper we describe a fast, specialized hardware implementation of a super-resolution algorithm for plenoptic cameras. The algorithm has been designed for field programmable graphic array (FPGA) devices using VHDL (very high speed integrated circuit (VHSIC) hardware description language). With this technology, we obtain an acceleration of several orders of magnitude using its extremely high-performance signal processing capability through parallelism and pipeline architecture. The system has been developed using generics of the VHDL language. This allows a very versatile and parameterizable system. The system user can easily modify parameters such as data width, number of microlenses of the plenoptic camera, their size and shape, and the super-resolution factor. The speed of the algorithm in FPGA has been successfully compared with the execution using a conventional computer for several image sizes and different 3D refocusing planes.

Super-resolved refocusing with a plenoptic camera

NASA Astrophysics Data System (ADS)

Zhou, Zhiliang; Yuan, Yan; Bin, Xiangli; Qian, Lulu

2011-03-01

This paper presents an approach to enhance the resolution of refocused images by super resolution methods. In plenoptic imaging, we demonstrate that the raw sensor image can be divided to a number of low-resolution angular images with sub-pixel shifts between each other. The sub-pixel shift, which defines the super-resolving ability, is mathematically derived by considering the plenoptic camera as equivalent camera arrays. We implement simulation to demonstrate the imaging process of a plenoptic camera. A high-resolution image is then reconstructed using maximum a posteriori (MAP) super resolution algorithms. Without other degradation effects in simulation, the super resolved image achieves a resolution as high as predicted by the proposed model. We also build an experimental setup to acquire light fields. With traditional refocusing methods, the image is rendered at a rather low resolution. In contrast, we implement the super-resolved refocusing methods and recover an image with more spatial details. To evaluate the performance of the proposed method, we finally compare the reconstructed images using image quality metrics like peak signal to noise ratio (PSNR).

Example-Based Super-Resolution Fluorescence Microscopy.

PubMed

Jia, Shu; Han, Boran; Kutz, J Nathan

2018-04-23

Capturing biological dynamics with high spatiotemporal resolution demands the advancement in imaging technologies. Super-resolution fluorescence microscopy offers spatial resolution surpassing the diffraction limit to resolve near-molecular-level details. While various strategies have been reported to improve the temporal resolution of super-resolution imaging, all super-resolution techniques are still fundamentally limited by the trade-off associated with the longer image acquisition time that is needed to achieve higher spatial information. Here, we demonstrated an example-based, computational method that aims to obtain super-resolution images using conventional imaging without increasing the imaging time. With a low-resolution image input, the method provides an estimate of its super-resolution image based on an example database that contains super- and low-resolution image pairs of biological structures of interest. The computational imaging of cellular microtubules agrees approximately with the experimental super-resolution STORM results. This new approach may offer potential improvements in temporal resolution for experimental super-resolution fluorescence microscopy and provide a new path for large-data aided biomedical imaging.

Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images.

PubMed

Fan, Chong; Chen, Xushuai; Zhong, Lei; Zhou, Min; Shi, Yun; Duan, Yulin

2017-03-18

A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line.

Optimized multiple linear mappings for single image super-resolution

NASA Astrophysics Data System (ADS)

Zhang, Kaibing; Li, Jie; Xiong, Zenggang; Liu, Xiuping; Gao, Xinbo

2017-12-01

Learning piecewise linear regression has been recognized as an effective way for example learning-based single image super-resolution (SR) in literature. In this paper, we employ an expectation-maximization (EM) algorithm to further improve the SR performance of our previous multiple linear mappings (MLM) based SR method. In the training stage, the proposed method starts with a set of linear regressors obtained by the MLM-based method, and then jointly optimizes the clustering results and the low- and high-resolution subdictionary pairs for regression functions by using the metric of the reconstruction errors. In the test stage, we select the optimal regressor for SR reconstruction by accumulating the reconstruction errors of m-nearest neighbors in the training set. Thorough experimental results carried on six publicly available datasets demonstrate that the proposed SR method can yield high-quality images with finer details and sharper edges in terms of both quantitative and perceptual image quality assessments.

Color image guided depth image super resolution using fusion filter

NASA Astrophysics Data System (ADS)

He, Jin; Liang, Bin; He, Ying; Yang, Jun

2018-04-01

Depth cameras are currently playing an important role in many areas. However, most of them can only obtain lowresolution (LR) depth images. Color cameras can easily provide high-resolution (HR) color images. Using color image as a guide image is an efficient way to get a HR depth image. In this paper, we propose a depth image super resolution (SR) algorithm, which uses a HR color image as a guide image and a LR depth image as input. We use the fusion filter of guided filter and edge based joint bilateral filter to get HR depth image. Our experimental results on Middlebury 2005 datasets show that our method can provide better quality in HR depth images both numerically and visually.

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

NASA Technical Reports Server (NTRS)

Mareboyana, Manohar; Le Moigne-Stewart, Jacqueline; Bennett, Jerome

2016-01-01

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ

PubMed Central

Müller, Marcel; Mönkemöller, Viola; Hennig, Simon; Hübner, Wolfgang; Huser, Thomas

2016-01-01

Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses. PMID:26996201

Experimental Study of Super-Resolution Using a Compressive Sensing Architecture

DTIC Science & Technology

2015-03-01

Intelligence 24(9), 1167–1183 (2002). [3] Lin, Z. and Shum, H.-Y., “Fundamental limits of reconstruction-based superresolution algorithms under local...IEEE Transactions on 52, 1289–1306 (April 2006). [9] Marcia, R. and Willett, R., “Compressive coded aperture superresolution image reconstruction,” in

Resolution enhancement of low-quality videos using a high-resolution frame

NASA Astrophysics Data System (ADS)

Pham, Tuan Q.; van Vliet, Lucas J.; Schutte, Klamer

2006-01-01

This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video together with a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR still image, high-frequency details are transferred from the HR source to the LR video. The DCT-domain algorithm is much faster than example-based SR in spatial domain 6 because of a reduction in search dimensionality, which is a direct result of the compact and uncorrelated DCT representation. Fast searching techniques like tree-structure vector quantization 16 and coherence search1 are also key to the improved efficiency. Preliminary results on MJPEG sequence show promising result of the DCT-domain SR synthesis approach.

Super-resolution processing for multi-functional LPI waveforms

NASA Astrophysics Data System (ADS)

Li, Zhengzheng; Zhang, Yan; Wang, Shang; Cai, Jingxiao

2014-05-01

Super-resolution (SR) is a radar processing technique closely related to the pulse compression (or correlation receiver). There are many super-resolution algorithms developed for the improved range resolution and reduced sidelobe contaminations. Traditionally, the waveforms used for the SR have been either phase-coding (such as LKP3 code, Barker code) or the frequency modulation (chirp, or nonlinear frequency modulation). There are, however, an important class of waveforms which are either random in nature (such as random noise waveform), or randomly modulated for multiple function operations (such as the ADS-B radar signals in [1]). These waveforms have the advantages of low-probability-of-intercept (LPI). If the existing SR techniques can be applied to these waveforms, there will be much more flexibility for using these waveforms in actual sensing missions. Also, SR usually has great advantage that the final output (as estimation of ground truth) is largely independent of the waveform. Such benefits are attractive to many important primary radar applications. In this paper the general introduction of the SR algorithms are provided first, and some implementation considerations are discussed. The selected algorithms are applied to the typical LPI waveforms, and the results are discussed. It is observed that SR algorithms can be reliably used for LPI waveforms, on the other hand, practical considerations should be kept in mind in order to obtain the optimal estimation results.

qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells.

PubMed

Andrews, J O; Conway, W; Cho, W -K; Narayanan, A; Spille, J -H; Jayanth, N; Inoue, T; Mullen, S; Thaler, J; Cissé, I I

2018-05-09

We present qSR, an analytical tool for the quantitative analysis of single molecule based super-resolution data. The software is created as an open-source platform integrating multiple algorithms for rigorous spatial and temporal characterizations of protein clusters in super-resolution data of living cells. First, we illustrate qSR using a sample live cell data of RNA Polymerase II (Pol II) as an example of highly dynamic sub-diffractive clusters. Then we utilize qSR to investigate the organization and dynamics of endogenous RNA Polymerase I (Pol I) in live human cells, throughout the cell cycle. Our analysis reveals a previously uncharacterized transient clustering of Pol I. Both stable and transient populations of Pol I clusters co-exist in individual living cells, and their relative fraction vary during cell cycle, in a manner correlating with global gene expression. Thus, qSR serves to facilitate the study of protein organization and dynamics with very high spatial and temporal resolutions directly in live cell.

Sub-pixel mapping of hyperspectral imagery using super-resolution

NASA Astrophysics Data System (ADS)

Sharma, Shreya; Sharma, Shakti; Buddhiraju, Krishna M.

2016-04-01

With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth's surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth's surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.

3D super resolution range-gated imaging for canopy reconstruction and measurement

NASA Astrophysics Data System (ADS)

Huang, Hantao; Wang, Xinwei; Sun, Liang; Lei, Pingshun; Fan, Songtao; Zhou, Yan

2018-01-01

In this paper, we proposed a method of canopy reconstruction and measurement based on 3D super resolution range-gated imaging. In this method, high resolution 2D intensity images are grasped by active gate imaging, and 3D images of canopy are reconstructed by triangular-range-intensity correlation algorithm at the same time. A range-gated laser imaging system(RGLIS) is established based on 808 nm diode laser and gated intensified charge-coupled device (ICCD) camera with 1392´1040 pixels. The proof experiments have been performed for potted plants located 75m away and trees located 165m away. The experiments show it that can acquire more than 1 million points per frame, and 3D imaging has the spatial resolution about 0.3mm at the distance of 75m and the distance accuracy about 10 cm. This research is beneficial for high speed acquisition of canopy structure and non-destructive canopy measurement.

Adaptive Markov Random Fields for Example-Based Super-resolution of Faces

NASA Astrophysics Data System (ADS)

Stephenson, Todd A.; Chen, Tsuhan

2006-12-01

Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.

A multi-emitter fitting algorithm for potential live cell super-resolution imaging over a wide range of molecular densities.

PubMed

Takeshima, T; Takahashi, T; Yamashita, J; Okada, Y; Watanabe, S

2018-05-25

Multi-emitter fitting algorithms have been developed to improve the temporal resolution of single-molecule switching nanoscopy, but the molecular density range they can analyse is narrow and the computation required is intensive, significantly limiting their practical application. Here, we propose a computationally fast method, wedged template matching (WTM), an algorithm that uses a template matching technique to localise molecules at any overlapping molecular density from sparse to ultrahigh density with subdiffraction resolution. WTM achieves the localization of overlapping molecules at densities up to 600 molecules μm -2 with a high detection sensitivity and fast computational speed. WTM also shows localization precision comparable with that of DAOSTORM (an algorithm for high-density super-resolution microscopy), at densities up to 20 molecules μm -2 , and better than DAOSTORM at higher molecular densities. The application of WTM to a high-density biological sample image demonstrated that it resolved protein dynamics from live cell images with subdiffraction resolution and a temporal resolution of several hundred milliseconds or less through a significant reduction in the number of camera images required for a high-density reconstruction. WTM algorithm is a computationally fast, multi-emitter fitting algorithm that can analyse over a wide range of molecular densities. The algorithm is available through the website. https://doi.org/10.17632/bf3z6xpn5j.1. © 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

Particle tracking and extended object imaging by interferometric super resolution microscopy

NASA Astrophysics Data System (ADS)

Gdor, Itay; Yoo, Seunghwan; Wang, Xiaolei; Daddysman, Matthew; Wilton, Rosemarie; Ferrier, Nicola; Hereld, Mark; Cossairt, Oliver (Ollie); Katsaggelos, Aggelos; Scherer, Norbert F.

2018-02-01

An interferometric fluorescent microscope and a novel theoretic image reconstruction approach were developed and used to obtain super-resolution images of live biological samples and to enable dynamic real time tracking. The tracking utilizes the information stored in the interference pattern of both the illuminating incoherent light and the emitted light. By periodically shifting the interferometer phase and a phase retrieval algorithm we obtain information that allow localization with sub-2 nm axial resolution at 5 Hz.

Advanced Topics in Space Situational Awareness

DTIC Science & Technology

2007-11-07

34super-resolution." Such optical superresolution is characteristic of many model-based image processing algorithms, and reflects the incorporation of...Sampling Theorem," J. Opt. Soc. Am. A, vol. 24, 311-325 (2007). [39] S. Prasad, "Digital and Optical Superresolution of Low-Resolution Image Sequences," Un...wavefront coding for the specific application of extension of image depth well beyond what is possible in a standard imaging system. The problem of optical

A weighted optimization approach to time-of-flight sensor fusion.

PubMed

Schwarz, Sebastian; Sjostrom, Marten; Olsson, Roger

2014-01-01

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Single image super resolution algorithm based on edge interpolation in NSCT domain

NASA Astrophysics Data System (ADS)

Zhang, Mengqun; Zhang, Wei; He, Xinyu

2017-11-01

In order to preserve the texture and edge information and to improve the space resolution of single frame, a superresolution algorithm based on Contourlet (NSCT) is proposed. The original low resolution image is transformed by NSCT, and the directional sub-band coefficients of the transform domain are obtained. According to the scale factor, the high frequency sub-band coefficients are amplified by the interpolation method based on the edge direction to the desired resolution. For high frequency sub-band coefficients with noise and weak targets, Bayesian shrinkage is used to calculate the threshold value. The coefficients below the threshold are determined by the correlation among the sub-bands of the same scale to determine whether it is noise and de-noising. The anisotropic diffusion filter is used to effectively enhance the weak target in the low contrast region of the target and background. Finally, the high-frequency sub-band is amplified by the bilinear interpolation method to the desired resolution, and then combined with the high-frequency subband coefficients after de-noising and small target enhancement, the NSCT inverse transform is used to obtain the desired resolution image. In order to verify the effectiveness of the proposed algorithm, the proposed algorithm and several common image reconstruction methods are used to test the synthetic image, motion blurred image and hyperspectral image, the experimental results show that compared with the traditional single resolution algorithm, the proposed algorithm can obtain smooth edges and good texture features, and the reconstructed image structure is well preserved and the noise is suppressed to some extent.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Super-resolution reconstruction for 4D computed tomography of the lung via the projections onto convex sets approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yu, E-mail: yuzhang@smu.edu.cn, E-mail: qianjinfeng08@gmail.com; Wu, Xiuxiu; Yang, Wei

2014-11-01

Purpose: The use of 4D computed tomography (4D-CT) of the lung is important in lung cancer radiotherapy for tumor localization and treatment planning. Sometimes, dense sampling is not acquired along the superior–inferior direction. This disadvantage results in an interslice thickness that is much greater than in-plane voxel resolutions. Isotropic resolution is necessary for multiplanar display, but the commonly used interpolation operation blurs images. This paper presents a super-resolution (SR) reconstruction method to enhance 4D-CT resolution. Methods: The authors assume that the low-resolution images of different phases at the same position can be regarded as input “frames” to reconstruct high-resolution images.more » The SR technique is used to recover high-resolution images. Specifically, the Demons deformable registration algorithm is used to estimate the motion field between different “frames.” Then, the projection onto convex sets approach is implemented to reconstruct high-resolution lung images. Results: The performance of the SR algorithm is evaluated using both simulated and real datasets. Their method can generate clearer lung images and enhance image structure compared with cubic spline interpolation and back projection (BP) method. Quantitative analysis shows that the proposed algorithm decreases the root mean square error by 40.8% relative to cubic spline interpolation and 10.2% versus BP. Conclusions: A new algorithm has been developed to improve the resolution of 4D-CT. The algorithm outperforms the cubic spline interpolation and BP approaches by producing images with markedly improved structural clarity and greatly reduced artifacts.« less

Experimental assessment and analysis of super-resolution in fluorescence microscopy based on multiple-point spread function fitting of spectrally demultiplexed images

NASA Astrophysics Data System (ADS)

Nishimura, Takahiro; Kimura, Hitoshi; Ogura, Yusuke; Tanida, Jun

2018-06-01

This paper presents an experimental assessment and analysis of super-resolution microscopy based on multiple-point spread function fitting of spectrally demultiplexed images using a designed DNA structure as a test target. For the purpose, a DNA structure was designed to have binding sites at a certain interval that is smaller than the diffraction limit. The structure was labeled with several types of quantum dots (QDs) to acquire their spatial information as spectrally encoded images. The obtained images are analyzed with a point spread function multifitting algorithm to determine the QD locations that indicate the binding site positions. The experimental results show that the labeled locations can be observed beyond the diffraction-limited resolution using three-colored fluorescence images that were obtained with a confocal fluorescence microscope. Numerical simulations show that labeling with eight types of QDs enables the positions aligned at 27.2-nm pitches on the DNA structure to be resolved with high accuracy.

Efficient Spatiotemporal Clutter Rejection and Nonlinear Filtering-based Dim Resolved and Unresolved Object Tracking Algorithms

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Tong, M.; Brown, A. P.; Agh, C.

2013-09-01

We develop efficient spatiotemporal image processing algorithms for rejection of non-stationary clutter and tracking of multiple dim objects using non-linear track-before-detect methods. For clutter suppression, we include an innovative image alignment (registration) algorithm. The images are assumed to contain elements of the same scene, but taken at different angles, from different locations, and at different times, with substantial clutter non-stationarity. These challenges are typical for space-based and surface-based IR/EO moving sensors, e.g., highly elliptical orbit or low earth orbit scenarios. The algorithm assumes that the images are related via a planar homography, also known as the projective transformation. The parameters are estimated in an iterative manner, at each step adjusting the parameter vector so as to achieve improved alignment of the images. Operating in the parameter space rather than in the coordinate space is a new idea, which makes the algorithm more robust with respect to noise as well as to large inter-frame disturbances, while operating at real-time rates. For dim object tracking, we include new advancements to a particle non-linear filtering-based track-before-detect (TrbD) algorithm. The new TrbD algorithm includes both real-time full image search for resolved objects not yet in track and joint super-resolution and tracking of individual objects in closely spaced object (CSO) clusters. The real-time full image search provides near-optimal detection and tracking of multiple extremely dim, maneuvering objects/clusters. The super-resolution and tracking CSO TrbD algorithm provides efficient near-optimal estimation of the number of unresolved objects in a CSO cluster, as well as the locations, velocities, accelerations, and intensities of the individual objects. We demonstrate that the algorithm is able to accurately estimate the number of CSO objects and their locations when the initial uncertainty on the number of objects is large. We demonstrate performance of the TrbD algorithm both for satellite-based and surface-based EO/IR surveillance scenarios.

Image quality improvement in cone-beam CT using the super-resolution technique.

PubMed

Oyama, Asuka; Kumagai, Shinobu; Arai, Norikazu; Takata, Takeshi; Saikawa, Yusuke; Shiraishi, Kenshiro; Kobayashi, Takenori; Kotoku, Jun'ichi

2018-04-05

This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.

Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging

PubMed Central

Dempsey, Graham T.; Vaughan, Joshua C.; Chen, Kok Hao; Bates, Mark; Zhuang, Xiaowei

2011-01-01

One approach to super-resolution fluorescence imaging uses sequential activation and localization of individual fluorophores to achieve high spatial resolution. Essential to this technique is the choice of fluorescent probes — the properties of the probes, including photons per switching event, on/off duty cycle, photostability, and number of switching cycles, largely dictate the quality of super-resolution images. While many probes have been reported, a systematic characterization of the properties of these probes and their impact on super-resolution image quality has been described in only a few cases. Here, we quantitatively characterized the switching properties of 26 organic dyes and directly related these properties to the quality of super-resolution images. This analysis provides a set of guidelines for characterization of super-resolution probes and a resource for selecting probes based on performance. Our evaluation identified several photoswitchable dyes with good to excellent performance in four independent spectral ranges, with which we demonstrated low crosstalk, four-color super-resolution imaging. PMID:22056676

Super resolution for astronomical observations

NASA Astrophysics Data System (ADS)

Li, Zhan; Peng, Qingyu; Bhanu, Bir; Zhang, Qingfeng; He, Haifeng

2018-05-01

In order to obtain detailed information from multiple telescope observations a general blind super-resolution (SR) reconstruction approach for astronomical images is proposed in this paper. A pixel-reliability-based SR reconstruction algorithm is described and implemented, where the developed process incorporates flat field correction, automatic star searching and centering, iterative star matching, and sub-pixel image registration. Images captured by the 1-m telescope at Yunnan Observatory are used to test the proposed technique. The results of these experiments indicate that, following SR reconstruction, faint stars are more distinct, bright stars have sharper profiles, and the backgrounds have higher details; thus these results benefit from the high-precision star centering and image registration provided by the developed method. Application of the proposed approach not only provides more opportunities for new discoveries from astronomical image sequences, but will also contribute to enhancing the capabilities of most spatial or ground-based telescopes.

Single-snapshot DOA estimation by using Compressed Sensing

NASA Astrophysics Data System (ADS)

Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin

2014-12-01

This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

The super-resolution debate

NASA Astrophysics Data System (ADS)

Won, Rachel

2018-05-01

In the quest for nanoscopy with super-resolution, consensus from the imaging community is that super-resolution is not always needed and that scientists should choose an imaging technique based on their specific application.

Pixel-super-resolved lensfree holography using adaptive relaxation factor and positional error correction

NASA Astrophysics Data System (ADS)

Zhang, Jialin; Chen, Qian; Sun, Jiasong; Li, Jiaji; Zuo, Chao

2018-01-01

Lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and field-of-view (FOV) of conventional lens-based microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). In this paper, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method to address the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Furthermore, an automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target across a wide imaging area of {29.85 mm2 and achieve half-pitch lateral resolution of 770 nm, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67 μm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Super-resolution for scanning light stimulation systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bitzer, L. A.; Neumann, K.; Benson, N., E-mail: niels.benson@uni-due.de

Super-resolution (SR) is a technique used in digital image processing to overcome the resolution limitation of imaging systems. In this process, a single high resolution image is reconstructed from multiple low resolution images. SR is commonly used for CCD and CMOS (Complementary Metal-Oxide-Semiconductor) sensor images, as well as for medical applications, e.g., magnetic resonance imaging. Here, we demonstrate that super-resolution can be applied with scanning light stimulation (LS) systems, which are common to obtain space-resolved electro-optical parameters of a sample. For our purposes, the Projection Onto Convex Sets (POCS) was chosen and modified to suit the needs of LS systems.more » To demonstrate the SR adaption, an Optical Beam Induced Current (OBIC) LS system was used. The POCS algorithm was optimized by means of OBIC short circuit current measurements on a multicrystalline solar cell, resulting in a mean square error reduction of up to 61% and improved image quality.« less

Super-resolved Parallel MRI by Spatiotemporal Encoding

PubMed Central

Schmidt, Rita; Baishya, Bikash; Ben-Eliezer, Noam; Seginer, Amir; Frydman, Lucio

2016-01-01

Recent studies described an alternative “ultrafast” scanning method based on spatiotemporal (SPEN) principles. SPEN demonstrates numerous potential advantages over EPI-based alternatives, at no additional expense in experimental complexity. An important aspect that SPEN still needs to achieve for providing a competitive acquisition alternative entails exploiting parallel imaging algorithms, without compromising its proven capabilities. The present work introduces a combination of multi-band frequency-swept pulses simultaneously encoding multiple, partial fields-of-view; together with a new algorithm merging a Super-Resolved SPEN image reconstruction and SENSE multiple-receiving methods. The ensuing approach enables one to reduce both the excitation and acquisition times of ultrafast SPEN acquisitions by the customary acceleration factor R, without compromises in either the ensuing spatial resolution, SAR deposition, or the capability to operate in multi-slice mode. The performance of these new single-shot imaging sequences and their ancillary algorithms were explored on phantoms and human volunteers at 3T. The gains of the parallelized approach were particularly evident when dealing with heterogeneous systems subject to major T2/T2* effects, as is the case upon single-scan imaging near tissue/air interfaces. PMID:24120293

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array.

PubMed

Bishara, Waheb; Sikora, Uzair; Mudanyali, Onur; Su, Ting-Wei; Yaglidere, Oguzhan; Luckhart, Shirley; Ozcan, Aydogan

2011-04-07

We report a portable lensless on-chip microscope that can achieve <1 µm resolution over a wide field-of-view of ∼ 24 mm(2) without the use of any mechanical scanning. This compact on-chip microscope weighs ∼ 95 g and is based on partially coherent digital in-line holography. Multiple fiber-optic waveguides are butt-coupled to light emitting diodes, which are controlled by a low-cost micro-controller to sequentially illuminate the sample. The resulting lensfree holograms are then captured by a digital sensor-array and are rapidly processed using a pixel super-resolution algorithm to generate much higher resolution holographic images (both phase and amplitude) of the objects. This wide-field and high-resolution on-chip microscope, being compact and light-weight, would be important for global health problems such as diagnosis of infectious diseases in remote locations. Toward this end, we validate the performance of this field-portable microscope by imaging human malaria parasites (Plasmodium falciparum) in thin blood smears. Our results constitute the first-time that a lensfree on-chip microscope has successfully imaged malaria parasites.

Computational-optical microscopy for 3D biological imaging beyond the diffraction limit

NASA Astrophysics Data System (ADS)

Grover, Ginni

In recent years, super-resolution imaging has become an important fluorescent microscopy tool. It has enabled imaging of structures smaller than the optical diffraction limit with resolution less than 50 nm. Extension to high-resolution volume imaging has been achieved by integration with various optical techniques. In this thesis, development of a fluorescent microscope to enable high resolution, extended depth, three dimensional (3D) imaging is discussed; which is achieved by integration of computational methods with optical systems. In the first part of the thesis, point spread function (PSF) engineering for volume imaging is discussed. A class of PSFs, referred to as double-helix (DH) PSFs, is generated. The PSFs exhibit two focused spots in the image plane which rotate about the optical axis, encoding depth in rotation of the image. These PSFs extend the depth-of-field up to a factor of ˜5. Precision performance of the DH-PSFs, based on an information theoretical analysis, is compared with other 3D methods with conclusion that the DH-PSFs provide the best precision and the longest depth-of-field. Out of various possible DH-PSFs, a suitable PSF is obtained for super-resolution microscopy. The DH-PSFs are implemented in imaging systems, such as a microscope, with a special phase modulation at the pupil plane. Surface-relief elements which are polarization-insensitive and ˜90% light efficient are developed for phase modulation. The photon-efficient DH-PSF microscopes thus developed are used, along with optimal position estimation algorithms, for tracking and super-resolution imaging in 3D. Imaging at depths-of-field of up to 2.5 microm is achieved without focus scanning. Microtubules were imaged with 3D resolution of (6, 9, 39) nm, which is in close agreement with the theoretical limit. A quantitative study of co-localization of two proteins in volume was conducted in live bacteria. In the last part of the thesis practical aspects of the DH-PSF microscope are discussed. A method to stabilize it, for extended periods of time, with 3-4 nm precision in 3D is developed. 3D Super-resolution is demonstrated without drift. A PSF correction algorithm is demonstrated to improve characteristics of the DH-PSF in an experiment, where it is implemented with a polarization-insensitive liquid crystal spatial light modulator.

Computational microscopy: illumination coding and nonlinear optimization enables gigapixel 3D phase imaging

NASA Astrophysics Data System (ADS)

Tian, Lei; Waller, Laura

2017-05-01

Microscope lenses can have either large field of view (FOV) or high resolution, not both. Computational microscopy based on illumination coding circumvents this limit by fusing images from different illumination angles using nonlinear optimization algorithms. The result is a Gigapixel-scale image having both wide FOV and high resolution. We demonstrate an experimentally robust reconstruction algorithm based on a 2nd order quasi-Newton's method, combined with a novel phase initialization scheme. To further extend the Gigapixel imaging capability to 3D, we develop a reconstruction method to process the 4D light field measurements from sequential illumination scanning. The algorithm is based on a 'multislice' forward model that incorporates both 3D phase and diffraction effects, as well as multiple forward scatterings. To solve the inverse problem, an iterative update procedure that combines both phase retrieval and 'error back-propagation' is developed. To avoid local minimum solutions, we further develop a novel physical model-based initialization technique that accounts for both the geometric-optic and 1st order phase effects. The result is robust reconstructions of Gigapixel 3D phase images having both wide FOV and super resolution in all three dimensions. Experimental results from an LED array microscope were demonstrated.

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

PubMed

Stylianidou, Stella; Brennan, Connor; Nissen, Silas B; Kuwada, Nathan J; Wiggins, Paul A

2016-11-01

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. © 2016 John Wiley & Sons Ltd.

Three-Dimensional Super-Resolution: Theory, Modeling, and Field Tests Results

NASA Technical Reports Server (NTRS)

Bulyshev, Alexander; Amzajerdian, Farzin; Roback, Vincent E.; Hines, Glenn; Pierrottet, Diego; Reisse, Robert

2014-01-01

Many flash lidar applications continue to demand higher three-dimensional image resolution beyond the current state-of-the-art technology of the detector arrays and their associated readout circuits. Even with the available number of focal plane pixels, the required number of photons for illuminating all the pixels may impose impractical requirements on the laser pulse energy or the receiver aperture size. Therefore, image resolution enhancement by means of a super-resolution algorithm in near real time presents a very attractive solution for a wide range of flash lidar applications. This paper describes a superresolution technique and illustrates its performance and merits for generating three-dimensional image frames at a video rate.

Enhancing multi-spot structured illumination microscopy with fluorescence difference

NASA Astrophysics Data System (ADS)

Ward, Edward N.; Torkelsen, Frida H.; Pal, Robert

2018-03-01

Structured illumination microscopy is a super-resolution technique used extensively in biological research. However, this technique is limited in the maximum possible resolution increase. Here we report the results of simulations of a novel enhanced multi-spot structured illumination technique. This method combines the super-resolution technique of difference microscopy with structured illumination deconvolution. Initial results give at minimum a 1.4-fold increase in resolution over conventional structured illumination in a low-noise environment. This new technique also has the potential to be expanded to further enhance axial resolution with three-dimensional difference microscopy. The requirement for precise pattern determination in this technique also led to the development of a new pattern estimation algorithm which proved more efficient and reliable than other methods tested.

Fast myopic 2D-SIM super resolution microscopy with joint modulation pattern estimation

NASA Astrophysics Data System (ADS)

Orieux, François; Loriette, Vincent; Olivo-Marin, Jean-Christophe; Sepulveda, Eduardo; Fragola, Alexandra

2017-12-01

Super-resolution in structured illumination microscopy (SIM) is obtained through de-aliasing of modulated raw images, in which high frequencies are measured indirectly inside the optical transfer function. Usual approaches that use 9 or 15 images are often too slow for dynamic studies. Moreover, as experimental conditions change with time, modulation parameters must be estimated within the images. This paper tackles the problem of image reconstruction for fast super resolution in SIM, where the number of available raw images is reduced to four instead of nine or fifteen. Within an optimization framework, the solution is inferred via a joint myopic criterion for image and modulation (or acquisition) parameters, leading to what is frequently called a myopic or semi-blind inversion problem. The estimate is chosen as the minimizer of the nonlinear criterion, numerically calculated by means of a block coordinate optimization algorithm. The effectiveness of the proposed method is demonstrated for simulated and experimental examples. The results show precise estimation of the modulation parameters jointly with the reconstruction of the super resolution image. The method also shows its effectiveness for thick biological samples.

A super-resolution ultrasound method for brain vascular mapping

PubMed Central

O'Reilly, Meaghan A.; Hynynen, Kullervo

2013-01-01

Purpose: High-resolution vascular imaging has not been achieved in the brain due to limitations of current clinical imaging modalities. The authors present a method for transcranial ultrasound imaging of single micrometer-size bubbles within a tube phantom. Methods: Emissions from single bubbles within a tube phantom were mapped through an ex vivo human skull using a sparse hemispherical receiver array and a passive beamforming algorithm. Noninvasive phase and amplitude correction techniques were applied to compensate for the aberrating effects of the skull bone. The positions of the individual bubbles were estimated beyond the diffraction limit of ultrasound to produce a super-resolution image of the tube phantom, which was compared with microcomputed tomography (micro-CT). Results: The resulting super-resolution ultrasound image is comparable to results obtained via the micro-CT for small tissue specimen imaging. Conclusions: This method provides superior resolution to deep-tissue contrast ultrasound and has the potential to be extended to provide complete vascular network imaging in the brain. PMID:24320408

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

NASA Astrophysics Data System (ADS)

Li, Jun-Bao; Liu, Jing; Pan, Jeng-Shyang; Yao, Hongxun

2017-06-01

Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

An Analysis of Periodic Components in BL Lac Object S5 0716 +714 with MUSIC Method

NASA Astrophysics Data System (ADS)

Tang, J.

2012-01-01

Multiple signal classification (MUSIC) algorithms are introduced to the estimation of the period of variation of BL Lac objects.The principle of MUSIC spectral analysis method and theoretical analysis of the resolution of frequency spectrum using analog signals are included. From a lot of literatures, we have collected a lot of effective observation data of BL Lac object S5 0716 + 714 in V, R, I bands from 1994 to 2008. The light variation periods of S5 0716 +714 are obtained by means of the MUSIC spectral analysis method and periodogram spectral analysis method. There exist two major periods: (3.33±0.08) years and (1.24±0.01) years for all bands. The estimation of the period of variation of the algorithm based on the MUSIC spectral analysis method is compared with that of the algorithm based on the periodogram spectral analysis method. It is a super-resolution algorithm with small data length, and could be used to detect the period of variation of weak signals.

Enhancing multi-spot structured illumination microscopy with fluorescence difference

PubMed Central

Torkelsen, Frida H.

2018-01-01

Structured illumination microscopy is a super-resolution technique used extensively in biological research. However, this technique is limited in the maximum possible resolution increase. Here we report the results of simulations of a novel enhanced multi-spot structured illumination technique. This method combines the super-resolution technique of difference microscopy with structured illumination deconvolution. Initial results give at minimum a 1.4-fold increase in resolution over conventional structured illumination in a low-noise environment. This new technique also has the potential to be expanded to further enhance axial resolution with three-dimensional difference microscopy. The requirement for precise pattern determination in this technique also led to the development of a new pattern estimation algorithm which proved more efficient and reliable than other methods tested. PMID:29657751

North Twin Peak in super resolution

NASA Technical Reports Server (NTRS)

1997-01-01

This pair of images shows the result of taking a sequence of 25 identical exposures from the Imager for Mars Pathfinder (IMP) of the northern Twin Peak, with small camera motions, and processing them with the Super-Resolution algorithm developed at NASA's Ames Research Center.

The upper image is a representative input image, scaled up by a factor of five, with the pixel edges smoothed out for a fair comparison. The lower image allows significantly finer detail to be resolved.

Mars Pathfinder is the second in NASA's Discovery program of low-cost spacecraft with highly focused science goals. The Jet Propulsion Laboratory, Pasadena, CA, developed and manages the Mars Pathfinder mission for NASA's Office of Space Science, Washington, D.C. JPL is an operating division of the California Institute of Technology (Caltech). The Imager for Mars Pathfinder (IMP) was developed by the University of Arizona Lunar and Planetary Laboratory under contract to JPL. Peter Smith is the Principal Investigator.

The super-resolution research was conducted by Peter Cheeseman, Bob Kanefsky, Robin Hanson, and John Stutz of NASA's Ames Research Center, Mountain View, CA. More information on this technology is available on the Ames Super Resolution home page at

http://ic-www.arc.nasa.gov/ic/projects/bayes-group/ group/super-res/

Super-resolution fluorescence microscopy by stepwise optical saturation

PubMed Central

Zhang, Yide; Nallathamby, Prakash D.; Vigil, Genevieve D.; Khan, Aamir A.; Mason, Devon E.; Boerckel, Joel D.; Roeder, Ryan K.; Howard, Scott S.

2018-01-01

Super-resolution fluorescence microscopy is an important tool in biomedical research for its ability to discern features smaller than the diffraction limit. However, due to its difficult implementation and high cost, the super-resolution microscopy is not feasible in many applications. In this paper, we propose and demonstrate a saturation-based super-resolution fluorescence microscopy technique that can be easily implemented and requires neither additional hardware nor complex post-processing. The method is based on the principle of stepwise optical saturation (SOS), where M steps of raw fluorescence images are linearly combined to generate an image with a M-fold increase in resolution compared with conventional diffraction-limited images. For example, linearly combining (scaling and subtracting) two images obtained at regular powers extends the resolution by a factor of 1.4 beyond the diffraction limit. The resolution improvement in SOS microscopy is theoretically infinite but practically is limited by the signal-to-noise ratio. We perform simulations and experimentally demonstrate super-resolution microscopy with both one-photon (confocal) and multiphoton excitation fluorescence. We show that with the multiphoton modality, the SOS microscopy can provide super-resolution imaging deep in scattering samples. PMID:29675306

Image resolution enhancement via image restoration using neural network

NASA Astrophysics Data System (ADS)

Zhang, Shuangteng; Lu, Yihong

2011-04-01

Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant.

Super-Resolution Scanning Laser Microscopy Based on Virtually Structured Detection

PubMed Central

Zhi, Yanan; Wang, Benquan; Yao, Xincheng

2016-01-01

Light microscopy plays a key role in biological studies and medical diagnosis. The spatial resolution of conventional optical microscopes is limited to approximately half the wavelength of the illumination light as a result of the diffraction limit. Several approaches—including confocal microscopy, stimulated emission depletion microscopy, stochastic optical reconstruction microscopy, photoactivated localization microscopy, and structured illumination microscopy—have been established to achieve super-resolution imaging. However, none of these methods is suitable for the super-resolution ophthalmoscopy of retinal structures because of laser safety issues and inevitable eye movements. We recently experimentally validated virtually structured detection (VSD) as an alternative strategy to extend the diffraction limit. Without the complexity of structured illumination, VSD provides an easy, low-cost, and phase artifact–free strategy to achieve super-resolution in scanning laser microscopy. In this article we summarize the basic principles of the VSD method, review our demonstrated single-point and line-scan super-resolution systems, and discuss both technical challenges and the potential of VSD-based instrumentation for super-resolution ophthalmoscopy of the retina. PMID:27480461

Super-resolution in a defocused plenoptic camera: a wave-optics-based approach.

PubMed

Sahin, Erdem; Katkovnik, Vladimir; Gotchev, Atanas

2016-03-01

Plenoptic cameras enable the capture of a light field with a single device. However, with traditional light field rendering procedures, they can provide only low-resolution two-dimensional images. Super-resolution is considered to overcome this drawback. In this study, we present a super-resolution method for the defocused plenoptic camera (Plenoptic 1.0), where the imaging system is modeled using wave optics principles and utilizing low-resolution depth information of the scene. We are particularly interested in super-resolution of in-focus and near in-focus scene regions, which constitute the most challenging cases. The simulation results show that the employed wave-optics model makes super-resolution possible for such regions as long as sufficiently accurate depth information is available.

Super-resolution structured illumination in optically thick specimens without fluorescent tagging

NASA Astrophysics Data System (ADS)

Hoffman, Zachary R.; DiMarzio, Charles A.

2017-11-01

This research extends the work of Hoffman et al. to provide both sectioning and super-resolution using random patterns within thick specimens. Two methods of processing structured illumination in reflectance have been developed without the need for a priori knowledge of either the optical system or the modulation patterns. We explore the use of two deconvolution algorithms that assume either Gaussian or sparse priors. This paper will show that while both methods accomplish their intended objective, the sparse priors method provides superior resolution and contrast against all tested targets, providing anywhere from ˜1.6× to ˜2× resolution enhancement. The methods developed here can reasonably be implemented to work without a priori knowledge about the patterns or point spread function. Further, all experiments are run using an incoherent light source, unknown random modulation patterns, and without the use of fluorescent tagging. These additional modifications are challenging, but the generalization of these methods makes them prime candidates for clinical application, providing super-resolved noninvasive sectioning in vivo.

Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy.

PubMed

Zhang, Jialin; Sun, Jiasong; Chen, Qian; Li, Jiaji; Zuo, Chao

2017-09-18

High-resolution wide field-of-view (FOV) microscopic imaging plays an essential role in various fields of biomedicine, engineering, and physical sciences. As an alternative to conventional lens-based scanning techniques, lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and FOV of conventional microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). Here, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which can solve, or at least partially alleviate these limitations. Our approach addresses the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target (~29.85 mm 2 ) and achieve half-pitch lateral resolution of 770 nm, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67µm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.

Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks

NASA Astrophysics Data System (ADS)

Li, Zhengji; Teng, Qizhi; He, Xiaohai; Yue, Guihua; Wang, Zhengyong

2017-09-01

The parameter evaluation of reservoir rocks can help us to identify components and calculate the permeability and other parameters, and it plays an important role in the petroleum industry. Until now, computed tomography (CT) has remained an irreplaceable way to acquire the microstructure of reservoir rocks. During the evaluation and analysis, large samples and high-resolution images are required in order to obtain accurate results. Owing to the inherent limitations of CT, however, a large field of view results in low-resolution images, and high-resolution images entail a smaller field of view. Our method is a promising solution to these data collection limitations. In this study, a framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT. A single reservoir structure and its downgraded model are divided into a large number of 3D cubes of voxel pairs and these cube pairs are used to calculate two overcomplete dictionaries and the sparse-representation coefficients in order to estimate the high frequency component. Future more, to better result, a new feature extract method with combine BM4D together with Laplacian filter are introduced. In addition, we conducted a visual evaluation of the method, and used the PSNR and FSIM to evaluate it qualitatively.

Characterization of Urban Landscape Using Super-Resolution UAS Data, Multiple Textural Scales and Data-Mining Techniques

NASA Astrophysics Data System (ADS)

Voss, M.; Blundell, B.

2015-12-01

Characterization of urban environments is a high priority for the U.S. Army as battlespaces have transitioned from the predominantly open spaces of the 20th century to urban areas where soldiers have reduced situational awareness due to the diversity and density of their surroundings. Creating high-resolution urban terrain geospatial information will improve mission planning and soldier effectiveness. In this effort, super-resolution true-color imagery was collected with an Altivan NOVA unmanned aerial system over the Muscatatuck Urban Training Center near Butlerville, Indiana on September 16, 2014. Multispectral texture analysis using different algorithms was conducted for urban surface characterization at a variety of scales. Training samples extracted from the true-color and texture images. These data were processed using a variety of meta-algorithms with a decision tree classifier to create a high-resolution urban features map. In addition to improving accuracy over traditional image classification methods, this technique allowed the determination of the most significant textural scales in creating urban terrain maps for tactical exploitation.

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans.

PubMed

Jia, Yuanyuan; Gholipour, Ali; He, Zhongshi; Warfield, Simon K

2017-05-01

In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3-D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution (HR) 3-D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multiframe super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the HR slices and the low-resolution (LR) sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of overcomplete dictionaries that are used in a sparse-land local model to upsample the input scans. The upsampled images are then combined using wavelet fusion and error backprojection to reconstruct an image. Features are learned from the data and no extra training set is needed. Qualitative and quantitative analyses were conducted to evaluate the proposed algorithm using simulated and clinical MR scans. Experimental results show that the proposed algorithm achieves promising results in terms of peak signal-to-noise ratio, structural similarity image index, intensity profiles, and visualization of small structures obscured in the LR imaging process due to partial volume effects. Our novel SR algorithm outperforms the nonlocal means (NLM) method using self-similarity, NLM method using self-similarity and image prior, self-training dictionary learning-based SR method, averaging of upsampled scans, and the wavelet fusion method. Our SR algorithm can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.

Localization-based super-resolution imaging of cellular structures.

PubMed

Kanchanawong, Pakorn; Waterman, Clare M

2013-01-01

Fluorescence microscopy allows direct visualization of fluorescently tagged proteins within cells. However, the spatial resolution of conventional fluorescence microscopes is limited by diffraction to ~250 nm, prompting the development of super-resolution microscopy which offers resolution approaching the scale of single proteins, i.e., ~20 nm. Here, we describe protocols for single molecule localization-based super-resolution imaging, using focal adhesion proteins as an example and employing either photoswitchable fluorophores or photoactivatable fluorescent proteins. These protocols should also be easily adaptable to imaging a broad array of macromolecular assemblies in cells whose components can be fluorescently tagged and assemble into high density structures.

Deep learning massively accelerates super-resolution localization microscopy.

PubMed

Ouyang, Wei; Aristov, Andrey; Lelek, Mickaël; Hao, Xian; Zimmer, Christophe

2018-06-01

The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.

High-resolution reconstruction for terahertz imaging.

PubMed

Xu, Li-Min; Fan, Wen-Hui; Liu, Jia

2014-11-20

We present a high-resolution (HR) reconstruction model and algorithms for terahertz imaging, taking advantage of super-resolution methodology and algorithms. The algorithms used include projection onto a convex sets approach, iterative backprojection approach, Lucy-Richardson iteration, and 2D wavelet decomposition reconstruction. Using the first two HR reconstruction methods, we successfully obtain HR terahertz images with improved definition and lower noise from four low-resolution (LR) 22×24 terahertz images taken from our homemade THz-TDS system at the same experimental conditions with 1.0 mm pixel. Using the last two HR reconstruction methods, we transform one relatively LR terahertz image to a HR terahertz image with decreased noise. This indicates potential application of HR reconstruction methods in terahertz imaging with pulsed and continuous wave terahertz sources.

Super-resolution imaging applied to moving object tracking

NASA Astrophysics Data System (ADS)

Swalaganata, Galandaru; Ratna Sulistyaningrum, Dwi; Setiyono, Budi

2017-10-01

Moving object tracking in a video is a method used to detect and analyze changes that occur in an object that being observed. Visual quality and the precision of the tracked target are highly wished in modern tracking system. The fact that the tracked object does not always seem clear causes the tracking result less precise. The reasons are low quality video, system noise, small object, and other factors. In order to improve the precision of the tracked object especially for small object, we propose a two step solution that integrates a super-resolution technique into tracking approach. First step is super-resolution imaging applied into frame sequences. This step was done by cropping the frame in several frame or all of frame. Second step is tracking the result of super-resolution images. Super-resolution image is a technique to obtain high-resolution images from low-resolution images. In this research single frame super-resolution technique is proposed for tracking approach. Single frame super-resolution was a kind of super-resolution that it has the advantage of fast computation time. The method used for tracking is Camshift. The advantages of Camshift was simple calculation based on HSV color that use its histogram for some condition and color of the object varies. The computational complexity and large memory requirements required for the implementation of super-resolution and tracking were reduced and the precision of the tracked target was good. Experiment showed that integrate a super-resolution imaging into tracking technique can track the object precisely with various background, shape changes of the object, and in a good light conditions.

High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors

NASA Astrophysics Data System (ADS)

Mojica, Edson; Pertuz, Said; Arguello, Henry

2017-12-01

One of the main challenges in Computed Tomography (CT) is obtaining accurate reconstructions of the imaged object while keeping a low radiation dose in the acquisition process. In order to solve this problem, several researchers have proposed the use of compressed sensing for reducing the amount of measurements required to perform CT. This paper tackles the problem of designing high-resolution coded apertures for compressed sensing computed tomography. In contrast to previous approaches, we aim at designing apertures to be used with low-resolution detectors in order to achieve super-resolution. The proposed method iteratively improves random coded apertures using a gradient descent algorithm subject to constraints in the coherence and homogeneity of the compressive sensing matrix induced by the coded aperture. Experiments with different test sets show consistent results for different transmittances, number of shots and super-resolution factors.

Development and Characterization of a Dither-Based Super-Resolution Reconstruction Method for Fiber Imaging Arrays

NASA Astrophysics Data System (ADS)

Languirand, Eric Robert

Chemical imaging is an important tool for providing insight into function, role, and spatial distribution of analytes. This thesis describes the use of imaging fiber bundles (IFB) for super-resolution reconstruction using surface enhanced Raman scattering (SERS) showing improvement in resolution with arrayed bundles for the first time. Additionally this thesis describes characteristics of the IFB with regards to cross-talk as a function of aperture size. The first part of this thesis characterizes the IFB for both tapered and untapered bundles in terms of cross-talk. Cross-talk is defined as the amount of light leaking from a central fiber element in the imaging fiber bundle to surrounding fiber elements. To make this measurement ubiquitous for all imaging bundles, quantum dots were employed. Untapered and tapered IFB possess cross-talk of 2% or less, with fiber elements down to 32nm. The second part of this thesis employs a super resolution reconstruction algorithm using projection onto convex sets for resolution improvement. When using IFB arrays, the point spread function (PSF) of the array can be known accurately if the fiber elements over fill the pixel detector array. Therefore, the use of the known PSF compared to a general blurring kernel was evaluated. Relative increases in resolution of 12% and 2% at the 95% confidence level are found, when compared to a reference image, for the general blurring kernel and PSF, respectively. The third part of this thesis shows for the first time the use of SERS with a dithered IFB array coupled with super-resolution reconstruction. The resolution improvement across a step-edge is shown to be approximately 20% when compared to a reference image. This provides an additional means of increasing the resolution of fiber bundles beyond that of just tapering. Furthermore, this provides a new avenue for nanoscale imaging using these bundles. Lastly, synthetic data with varying degrees of signal-to-noise (S/N) were employed to explore the relationship S/N has with the reconstruction process. It is generally shown that increasing the number images used in the reconstruction process and increasing the S/N will improve the reconstruction providing larger increases in resolution.

Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs

NASA Astrophysics Data System (ADS)

Umehara, Kensuke; Ota, Junko; Ishimaru, Naoki; Ohno, Shunsuke; Okamoto, Kentaro; Suzuki, Takanori; Shirai, Naoki; Ishida, Takayuki

2017-02-01

Single image super-resolution (SR) method can generate a high-resolution (HR) image from a low-resolution (LR) image by enhancing image resolution. In medical imaging, HR images are expected to have a potential to provide a more accurate diagnosis with the practical application of HR displays. In recent years, the super-resolution convolutional neural network (SRCNN), which is one of the state-of-the-art deep learning based SR methods, has proposed in computer vision. In this study, we applied and evaluated the SRCNN scheme to improve the image quality of magnified images in chest radiographs. For evaluation, a total of 247 chest X-rays were sampled from the JSRT database. The 247 chest X-rays were divided into 93 training cases with non-nodules and 152 test cases with lung nodules. The SRCNN was trained using the training dataset. With the trained SRCNN, the HR image was reconstructed from the LR one. We compared the image quality of the SRCNN and conventional image interpolation methods, nearest neighbor, bilinear and bicubic interpolations. For quantitative evaluation, we measured two image quality metrics, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the SRCNN scheme, PSNR and SSIM were significantly higher than those of three interpolation methods (p<0.001). Visual assessment confirmed that the SRCNN produced much sharper edge than conventional interpolation methods without any obvious artifacts. These preliminary results indicate that the SRCNN scheme significantly outperforms conventional interpolation algorithms for enhancing image resolution and that the use of the SRCNN can yield substantial improvement of the image quality of magnified images in chest radiographs.

Two-photon speckle illumination for super-resolution microscopy.

PubMed

Negash, Awoke; Labouesse, Simon; Chaumet, Patrick C; Belkebir, Kamal; Giovannini, Hugues; Allain, Marc; Idier, Jérôme; Sentenac, Anne

2018-06-01

We present a numerical study of a microscopy setup in which the sample is illuminated with uncontrolled speckle patterns and the two-photon excitation fluorescence is collected on a camera. We show that, using a simple deconvolution algorithm for processing the speckle low-resolution images, this wide-field imaging technique exhibits resolution significantly better than that of two-photon excitation scanning microscopy or one-photon excitation bright-field microscopy.

Transiting Planet Search in the Kepler Pipeline

NASA Technical Reports Server (NTRS)

Jenkins, Jon M.; Chandrasekaran, Hema; McCauliff, Sean D.; Caldwell, Douglas A.; Tenebaum, Peter; Li, Jie; Klaus, Todd C.; Cote, Mile T.; Middour, Christopher

2010-01-01

The Kepler Mission simultaneously measures the brightness of more than 160,000 stars every 29.4 minutes over a 3.5-year mission to search for transiting planets. Detecting transits is a signal-detection problem where the signal of interest is a periodic pulse train and the predominant noise source is non-white, non-stationary (1/f) type process of stellar variability. Many stars also exhibit coherent or quasi-coherent oscillations. The detection algorithm first identifies and removes strong oscillations followed by an adaptive, wavelet-based matched filter. We discuss how we obtain super-resolution detection statistics and the effectiveness of the algorithm for Kepler flight data.

SRRF: Universal live-cell super-resolution microscopy.

PubMed

Culley, Siân; Tosheva, Kalina L; Matos Pereira, Pedro; Henriques, Ricardo

2018-08-01

Super-resolution microscopy techniques break the diffraction limit of conventional optical microscopy to achieve resolutions approaching tens of nanometres. The major advantage of such techniques is that they provide resolutions close to those obtainable with electron microscopy while maintaining the benefits of light microscopy such as a wide palette of high specificity molecular labels, straightforward sample preparation and live-cell compatibility. Despite this, the application of super-resolution microscopy to dynamic, living samples has thus far been limited and often requires specialised, complex hardware. Here we demonstrate how a novel analytical approach, Super-Resolution Radial Fluctuations (SRRF), is able to make live-cell super-resolution microscopy accessible to a wider range of researchers. We show its applicability to live samples expressing GFP using commercial confocal as well as laser- and LED-based widefield microscopes, with the latter achieving long-term timelapse imaging with minimal photobleaching. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

GPU-Accelerated Hybrid Algorithm for 3D Localization of Fluorescent Emitters in Dense Clusters

NASA Astrophysics Data System (ADS)

Jung, Yoon; Barsic, Anthony; Piestun, Rafael; Fakhri, Nikta

In stochastic switching-based super-resolution imaging, a random subset of fluorescent emitters are imaged and localized for each frame to construct a single high resolution image. However, the condition of non-overlapping point spread functions (PSFs) imposes constraints on experimental parameters. Recent development in post processing methods such as dictionary-based sparse support recovery using compressive sensing has shown up to an order of magnitude higher recall rate than single emitter fitting methods. However, the computational complexity of this approach scales poorly with the grid size and requires long runtime. Here, we introduce a fast and accurate compressive sensing algorithm for localizing fluorescent emitters in high density in 3D, namely sparse support recovery using Orthogonal Matching Pursuit (OMP) and L1-Homotopy algorithm for reconstructing STORM images (SOLAR STORM). SOLAR STORM combines OMP with L1-Homotopy to reduce computational complexity, which is further accelerated by parallel implementation using GPUs. This method can be used in a variety of experimental conditions for both in vitro and live cell fluorescence imaging.

Three-dimensional super-resolved live cell imaging through polarized multi-angle TIRF.

PubMed

Zheng, Cheng; Zhao, Guangyuan; Liu, Wenjie; Chen, Youhua; Zhang, Zhimin; Jin, Luhong; Xu, Yingke; Kuang, Cuifang; Liu, Xu

2018-04-01

Measuring three-dimensional nanoscale cellular structures is challenging, especially when the structure is dynamic. Owing to the informative total internal reflection fluorescence (TIRF) imaging under varied illumination angles, multi-angle (MA) TIRF has been examined to offer a nanoscale axial and a subsecond temporal resolution. However, conventional MA-TIRF still performs badly in lateral resolution and fails to characterize the depth image in densely distributed regions. Here, we emphasize the lateral super-resolution in the MA-TIRF, exampled by simply introducing polarization modulation into the illumination procedure. Equipped with a sparsity and accelerated proximal algorithm, we examine a more precise 3D sample structure compared with previous methods, enabling live cell imaging with a temporal resolution of 2 s and recovering high-resolution mitochondria fission and fusion processes. We also shared the recovery program, which is the first open-source recovery code for MA-TIRF, to the best of our knowledge.

Dynamic placement of plasmonic hotspots for super-resolution surface-enhanced Raman scattering.

PubMed

Ertsgaard, Christopher T; McKoskey, Rachel M; Rich, Isabel S; Lindquist, Nathan C

2014-10-28

In this paper, we demonstrate dynamic placement of locally enhanced plasmonic fields using holographic laser illumination of a silver nanohole array. To visualize these focused "hotspots", the silver surface was coated with various biological samples for surface-enhanced Raman spectroscopy (SERS) imaging. Due to the large field enhancements, blinking behavior of the SERS hotspots was observed and processed using a stochastic optical reconstruction microscopy algorithm enabling super-resolution localization of the hotspots to within 10 nm. These hotspots were then shifted across the surface in subwavelength (<100 nm for a wavelength of 660 nm) steps using holographic illumination from a spatial light modulator. This created a dynamic imaging and sensing surface, whereas static illumination would only have produced stationary hotspots. Using this technique, we also show that such subwavelength shifting and localization of plasmonic hotspots has potential for imaging applications. Interestingly, illuminating the surface with randomly shifting SERS hotspots was sufficient to completely fill in a wide field of view for super-resolution chemical imaging.

Computational wavelength resolution for in-line lensless holography: phase-coded diffraction patterns and wavefront group-sparsity

NASA Astrophysics Data System (ADS)

Katkovnik, Vladimir; Shevkunov, Igor; Petrov, Nikolay V.; Egiazarian, Karen

2017-06-01

In-line lensless holography is considered with a random phase modulation at the object plane. The forward wavefront propagation is modelled using the Fourier transform with the angular spectrum transfer function. The multiple intensities (holograms) recorded by the sensor are random due to the random phase modulation and noisy with Poissonian noise distribution. It is shown by computational experiments that high-accuracy reconstructions can be achieved with resolution going up to the two thirds of the wavelength. With respect to the sensor pixel size it is a super-resolution with a factor of 32. The algorithm designed for optimal superresolution phase/amplitude reconstruction from Poissonian data is based on the general methodology developed for phase retrieval with a pixel-wise resolution in V. Katkovnik, "Phase retrieval from noisy data based on sparse approximation of object phase and amplitude", http://www.cs.tut.fi/ lasip/DDT/index3.html.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture.

PubMed

Yamamoto, Kyosuke; Togami, Takashi; Yamaguchi, Norio

2017-11-06

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture-in cooperation with image processing technologies-for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

PubMed Central

Togami, Takashi; Yamaguchi, Norio

2017-01-01

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis. PMID:29113104

Multiband super-resolution imaging of graded-index photonic crystal flat lens

NASA Astrophysics Data System (ADS)

Xie, Jianlan; Wang, Junzhong; Ge, Rui; Yan, Bei; Liu, Exian; Tan, Wei; Liu, Jianjun

2018-05-01

Multiband super-resolution imaging of point source is achieved by a graded-index photonic crystal flat lens. With the calculations of six bands in common photonic crystal (CPC) constructed with scatterers of different refractive indices, it can be found that the super-resolution imaging of point source can be realized by different physical mechanisms in three different bands. In the first band, the imaging of point source is based on far-field condition of spherical wave while in the second band, it is based on the negative effective refractive index and exhibiting higher imaging quality than that of the CPC. However, in the fifth band, the imaging of point source is mainly based on negative refraction of anisotropic equi-frequency surfaces. The novel method of employing different physical mechanisms to achieve multiband super-resolution imaging of point source is highly meaningful for the field of imaging.

Multiframe super resolution reconstruction method based on light field angular images

NASA Astrophysics Data System (ADS)

Zhou, Shubo; Yuan, Yan; Su, Lijuan; Ding, Xiaomin; Wang, Jichao

2017-12-01

The plenoptic camera can directly obtain 4-dimensional light field information from a 2-dimensional sensor. However, based on the sampling theorem, the spatial resolution is greatly limited by the microlenses. In this paper, we present a method of reconstructing high-resolution images from the angular images. First, the ray tracing method is used to model the telecentric-based light field imaging process. Then, we analyze the subpixel shifts between the angular images extracted from the defocused light field data and the blur in the angular images. According to the analysis above, we construct the observation model from the ideal high-resolution image to the angular images. Applying the regularized super resolution method, we can obtain the super resolution result with a magnification ratio of 8. The results demonstrate the effectiveness of the proposed observation model.

Super-resolution image reconstruction from UAS surveillance video through affine invariant interest point-based motion estimation

NASA Astrophysics Data System (ADS)

He, Qiang; Schultz, Richard R.; Wang, Yi; Camargo, Aldo; Martel, Florent

2008-01-01

In traditional super-resolution methods, researchers generally assume that accurate subpixel image registration parameters are given a priori. In reality, accurate image registration on a subpixel grid is the single most critically important step for the accuracy of super-resolution image reconstruction. In this paper, we introduce affine invariant features to improve subpixel image registration, which considerably reduces the number of mismatched points and hence makes traditional image registration more efficient and more accurate for super-resolution video enhancement. Affine invariant interest points include those corners that are invariant to affine transformations, including scale, rotation, and translation. They are extracted from the second moment matrix through the integration and differentiation covariance matrices. Our tests are based on two sets of real video captured by a small Unmanned Aircraft System (UAS) aircraft, which is highly susceptible to vibration from even light winds. The experimental results from real UAS surveillance video show that affine invariant interest points are more robust to perspective distortion and present more accurate matching than traditional Harris/SIFT corners. In our experiments on real video, all matching affine invariant interest points are found correctly. In addition, for the same super-resolution problem, we can use many fewer affine invariant points than Harris/SIFT corners to obtain good super-resolution results.

A kind of color image segmentation algorithm based on super-pixel and PCNN

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Wang, YaWen; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Image segmentation is a very important step in the low-level visual computing. Although image segmentation has been studied for many years, there are still many problems. PCNN (Pulse Coupled Neural network) has biological background, when it is applied to image segmentation it can be viewed as a region-based method, but due to the dynamics properties of PCNN, many connectionless neurons will pulse at the same time, so it is necessary to identify different regions for further processing. The existing PCNN image segmentation algorithm based on region growing is used for grayscale image segmentation, cannot be directly used for color image segmentation. In addition, the super-pixel can better reserve the edges of images, and reduce the influences resulted from the individual difference between the pixels on image segmentation at the same time. Therefore, on the basis of the super-pixel, the original PCNN algorithm based on region growing is improved by this paper. First, the color super-pixel image was transformed into grayscale super-pixel image which was used to seek seeds among the neurons that hadn't been fired. And then it determined whether to stop growing by comparing the average of each color channel of all the pixels in the corresponding regions of the color super-pixel image. Experiment results show that the proposed algorithm for the color image segmentation is fast and effective, and has a certain effect and accuracy.

Super-pixel extraction based on multi-channel pulse coupled neural network

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction.

PubMed

Ravì, Daniele; Szczotka, Agnieszka Barbara; Shakir, Dzhoshkun Ismail; Pereira, Stephen P; Vercauteren, Tom

2018-06-01

Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

PubMed Central

Jain, Saurabh; Sima, Diana M.; Sanaei Nezhad, Faezeh; Hangel, Gilbert; Bogner, Wolfgang; Williams, Stephen; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

2017-01-01

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications. PMID:28197066

New learning based super-resolution: use of DWT and IGMRF prior.

PubMed

Gajjar, Prakash P; Joshi, Manjunath V

2010-05-01

In this paper, we propose a new learning-based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a database consisting of low and high spatial resolution images, we obtain super-resolution for the test image. We first obtain an initial high-resolution (HR) estimate by learning the high-frequency details from the available database. A new discrete wavelet transform (DWT) based approach is proposed for learning that uses a set of low-resolution (LR) images and their corresponding HR versions. Since the super-resolution is an ill-posed problem, we obtain the final solution using a regularization framework. The LR image is modeled as the aliased and noisy version of the corresponding HR image, and the aliasing matrix entries are estimated using the test image and the initial HR estimate. The prior model for the super-resolved image is chosen as an Inhomogeneous Gaussian Markov random field (IGMRF) and the model parameters are estimated using the same initial HR estimate. A maximum a posteriori (MAP) estimation is used to arrive at the cost function which is minimized using a simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting the experiments on gray scale as well as on color images. The method is compared with the standard interpolation technique and also with existing learning-based approaches. The proposed approach can be used in applications such as wildlife sensor networks, remote surveillance where the memory, the transmission bandwidth, and the camera cost are the main constraints.

Supporting lander and rover operation: a novel super-resolution restoration technique

NASA Astrophysics Data System (ADS)

Tao, Yu; Muller, Jan-Peter

2015-04-01

Higher resolution imaging data is always desirable to critical rover engineering operations, such as landing site selection, path planning, and optical localisation. For current Mars missions, 25cm HiRISE images have been widely used by the MER & MSL engineering team for rover path planning and location registration/adjustment. However, 25cm is not high enough resolution to be able to view individual rocks (≤2m in size) or visualise the types of sedimentary features that rover onboard cameras might observe. Nevertheless, due to various physical constraints (e.g. telescope size and mass) from the imaging instruments themselves, one needs to be able to tradeoff spatial resolution and bandwidth. This means that future imaging systems are likely to be limited to resolve features larger than 25cm. We have developed a novel super-resolution algorithm/pipeline to be able to restore higher resolution image from the non-redundant sub-pixel information contained in multiple lower resolution raw images [Tao & Muller 2015]. We will demonstrate with experiments performed using 5-10 overlapped 25cm HiRISE images for MER-A, MER-B & MSL to resolve 5-10cm super resolution images that can be directly compared to rover imagery at a range of 5 metres from the rover cameras but in our case can be used to visualise features many kilometres away from the actual rover traverse. We will demonstrate how these super-resolution images together with image understanding software can be used to quantify rock size-frequency distributions as well as measure sedimentary rock layers for several critical sites for comparison with rover orthorectified image mosaic to demonstrate optimality of using our super-resolution resolved image to better support future lander and rover operation in future. We present the potential of super-resolution for virtual exploration to the ˜400 HiRISE areas which have been viewed 5 or more times and the potential application of this technique to all of the ESA ExoMars Trace Gas orbiter CaSSiS stereo, multi-angle and colour camera images from 2017 onwards. Acknowledgements: The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement No.312377 PRoViDE.

Easy-DHPSF open-source software for three-dimensional localization of single molecules with precision beyond the optical diffraction limit.

PubMed

Lew, Matthew D; von Diezmann, Alexander R S; Moerner, W E

2013-02-25

Automated processing of double-helix (DH) microscope images of single molecules (SMs) streamlines the protocol required to obtain super-resolved three-dimensional (3D) reconstructions of ultrastructures in biological samples by single-molecule active control microscopy. Here, we present a suite of MATLAB subroutines, bundled with an easy-to-use graphical user interface (GUI), that facilitates 3D localization of single emitters (e.g. SMs, fluorescent beads, or quantum dots) with precisions of tens of nanometers in multi-frame movies acquired using a wide-field DH epifluorescence microscope. The algorithmic approach is based upon template matching for SM recognition and least-squares fitting for 3D position measurement, both of which are computationally expedient and precise. Overlapping images of SMs are ignored, and the precision of least-squares fitting is not as high as maximum likelihood-based methods. However, once calibrated, the algorithm can fit 15-30 molecules per second on a 3 GHz Intel Core 2 Duo workstation, thereby producing a 3D super-resolution reconstruction of 100,000 molecules over a 20×20×2 μm field of view (processing 128×128 pixels × 20000 frames) in 75 min.

Propagation phasor approach for holographic image reconstruction

PubMed Central

Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan

2016-01-01

To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671

The formation of quantum images and their transformation and super-resolution reading

NASA Astrophysics Data System (ADS)

Balakin, D. A.; Belinsky, A. V.

2016-05-01

Images formed by light with suppressed photon fluctuations are interesting objects for studies with the aim of increasing their limiting information capacity and quality. This light in the sub-Poisson state can be prepared in a resonator filled with a medium with Kerr nonlinearity, in which self-phase modulation takes place. Spatially and temporally multimode light beams are studied and the production of spatial frequency spectra of suppressed photon fluctuations is described. The efficient operation regimes of the system are found. A particular schematic solution is described, which allows one to realize the potential possibilities laid in the formation of the squeezed states of light to a maximum degree during self-phase modulation in a resonator for the maximal suppression of amplitude quantum noises upon two-dimensional imaging. The efficiency of using light with suppressed quantum fluctuations for computer image processing is studied. An algorithm is described for interpreting measurements for increasing the resolution with respect to the geometrical resolution. A mathematical model that characterizes the measurement scheme is constructed and the problem of the image reconstruction is solved. The algorithm for the interpretation of images is verified. Conditions are found for the efficient application of sub-Poisson light for super-resolution imaging. It is found that the image should have a low contrast and be maximally transparent.

Super resolution PLIF demonstrated in turbulent jet flows seeded with I2

NASA Astrophysics Data System (ADS)

Xu, Wenjiang; Liu, Ning; Ma, Lin

2018-05-01

Planar laser induced fluorescence (PLIF) represents an indispensable tool for flow and flame imaging. However, the PLIF technique suffers from limited spatial resolution or blurring in many situations, which restricts its applicability and capability. This work describes a new method, named SR-PLIF (super-resolution PLIF), to overcome these limitations and enhance the capability of PLIF. The method uses PLIF images captured simultaneously from two (or more) orientations to reconstruct a final PLIF image with resolution enhanced or blurring removed. This paper reports the development of the reconstruction algorithm, and the experimental demonstration of the SR-PLIF method both with controlled samples and with turbulent flows seeded with iodine vapor. Using controlled samples with two cameras, the spatial resolution in the best case was improved from 0.06 mm in the projections to 0.03 mm in the SR image, in terms of the spreading width of a sharp edge. With turbulent flows, an image sharpness measure was developed to quantify the spatial resolution, and SR reconstruction with two cameras can effectively improve the spatial resolution compared to the projections in terms of the sharpness measure.

Super-Resolution for Color Imagery

DTIC Science & Technology

2017-09-01

separately; however, it requires performing the super-resolution computation 3 times. We transform images in the default red, green, blue (RGB) color space...chrominance components based on ARL’s alias-free image upsampling using Fourier-based windowing methods. A reverse transformation is performed on... Transformation from sRGB to CIELAB............................................... 3 Fig. 2 YCbCr mathematical coordinate transformation

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy (iPALM).

PubMed

Wang, Yilin; Kanchanawong, Pakorn

2016-12-01

Fluorescence microscopy enables direct visualization of specific biomolecules within cells. However, for conventional fluorescence microscopy, the spatial resolution is restricted by diffraction to ~ 200 nm within the image plane and > 500 nm along the optical axis. As a result, fluorescence microscopy has long been severely limited in the observation of ultrastructural features within cells. The recent development of super resolution microscopy methods has overcome this limitation. In particular, the advent of photoswitchable fluorophores enables localization-based super resolution microscopy, which provides resolving power approaching the molecular-length scale. Here, we describe the application of a three-dimensional super resolution microscopy method based on single-molecule localization microscopy and multiphase interferometry, called interferometric PhotoActivated Localization Microscopy (iPALM). This method provides nearly isotropic resolution on the order of 20 nm in all three dimensions. Protocols for visualizing the filamentous actin cytoskeleton, including specimen preparation and operation of the iPALM instrument, are described here. These protocols are also readily adaptable and instructive for the study of other ultrastructural features in cells.

Super resolution terahertz imaging by subpixel estimation: application to hyperspectral beam profiling

NASA Astrophysics Data System (ADS)

Logofătu, Petre C.; Damian, Victor

2018-05-01

A super-resolution terahertz imaging technique based on subpixel estimation was applied to hyperspectral beam profiling. The topic of hyperspectral beam profiling was chosen because the beam profile and its dependence on wavelength are not well known and are important for imaging applications. Super-resolution is required here to avoid diffraction effects and to provide a stronger signal. Super-resolution usually adds supplementary information to the measurement, but in this case, it is a prerequisite for it. We report that the beam profile is almost Gaussian for many frequencies; the waist of the Gaussian profile increases with frequency while the center wobbles slightly. Knowledge of the beam profile may subsequently be used as reference for imaging.

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

PubMed Central

Yue, Bo; Wang, Shuang; Liang, Xuefeng; Jiao, Licheng; Xu, Caijin

2016-01-01

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. PMID:26927114

Super-resolution imaging based on the temperature-dependent electron-phonon collision frequency effect of metal thin films

NASA Astrophysics Data System (ADS)

Ding, Chenliang; Wei, Jingsong; Xiao, Mufei

2018-05-01

We herein propose a far-field super-resolution imaging with metal thin films based on the temperature-dependent electron-phonon collision frequency effect. In the proposed method, neither fluorescence labeling nor any special properties are required for the samples. The 100 nm lands and 200 nm grooves on the Blu-ray disk substrates were clearly resolved and imaged through a laser scanning microscope of wavelength 405 nm. The spot size was approximately 0.80 μm , and the imaging resolution of 1/8 of the laser spot size was experimentally obtained. This work can be applied to the far-field super-resolution imaging of samples with neither fluorescence labeling nor any special properties.

Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Liebel, L.; Körner, M.

2016-06-01

In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for single-image super resolution are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of deep learning techniques, such as convolutional neural networks (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, end-to-end learning is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.

Localization-based super-resolution imaging meets high-content screening.

PubMed

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

Multi-dimensional super-resolution imaging enables surface hydrophobicity mapping

NASA Astrophysics Data System (ADS)

Bongiovanni, Marie N.; Godet, Julien; Horrocks, Mathew H.; Tosatto, Laura; Carr, Alexander R.; Wirthensohn, David C.; Ranasinghe, Rohan T.; Lee, Ji-Eun; Ponjavic, Aleks; Fritz, Joelle V.; Dobson, Christopher M.; Klenerman, David; Lee, Steven F.

2016-12-01

Super-resolution microscopy allows biological systems to be studied at the nanoscale, but has been restricted to providing only positional information. Here, we show that it is possible to perform multi-dimensional super-resolution imaging to determine both the position and the environmental properties of single-molecule fluorescent emitters. The method presented here exploits the solvatochromic and fluorogenic properties of nile red to extract both the emission spectrum and the position of each dye molecule simultaneously enabling mapping of the hydrophobicity of biological structures. We validated this by studying synthetic lipid vesicles of known composition. We then applied both to super-resolve the hydrophobicity of amyloid aggregates implicated in neurodegenerative diseases, and the hydrophobic changes in mammalian cell membranes. Our technique is easily implemented by inserting a transmission diffraction grating into the optical path of a localization-based super-resolution microscope, enabling all the information to be extracted simultaneously from a single image plane.

Multi-dimensional super-resolution imaging enables surface hydrophobicity mapping

PubMed Central

Bongiovanni, Marie N.; Godet, Julien; Horrocks, Mathew H.; Tosatto, Laura; Carr, Alexander R.; Wirthensohn, David C.; Ranasinghe, Rohan T.; Lee, Ji-Eun; Ponjavic, Aleks; Fritz, Joelle V.; Dobson, Christopher M.; Klenerman, David; Lee, Steven F.

2016-01-01

Super-resolution microscopy allows biological systems to be studied at the nanoscale, but has been restricted to providing only positional information. Here, we show that it is possible to perform multi-dimensional super-resolution imaging to determine both the position and the environmental properties of single-molecule fluorescent emitters. The method presented here exploits the solvatochromic and fluorogenic properties of nile red to extract both the emission spectrum and the position of each dye molecule simultaneously enabling mapping of the hydrophobicity of biological structures. We validated this by studying synthetic lipid vesicles of known composition. We then applied both to super-resolve the hydrophobicity of amyloid aggregates implicated in neurodegenerative diseases, and the hydrophobic changes in mammalian cell membranes. Our technique is easily implemented by inserting a transmission diffraction grating into the optical path of a localization-based super-resolution microscope, enabling all the information to be extracted simultaneously from a single image plane. PMID:27929085

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

Temporally flickering nanoparticles for compound cellular imaging and super resolution

NASA Astrophysics Data System (ADS)

Ilovitsh, Tali; Danan, Yossef; Meir, Rinat; Meiri, Amihai; Zalevsky, Zeev

2016-03-01

This work presents the use of flickering nanoparticles for imaging biological samples. The method has high noise immunity, and it enables the detection of overlapping types of GNPs, at significantly sub-diffraction distances, making it attractive for super resolving localization microscopy techniques. The method utilizes a lock-in technique at which the imaging of the sample is done using a time-modulated laser beam that match the number of the types of gold nanoparticles (GNPs) that label a given sample, and resulting in the excitation of the temporal flickering of the scattered light at known temporal frequencies. The final image where the GNPs are spatially separated is obtained using post processing where the proper spectral components corresponding to the different modulation frequencies are extracted. This allows the simultaneous super resolved imaging of multiple types of GNPs that label targets of interest within biological samples. Additionally applying the post-processing algorithm of the K-factor image decomposition algorithm can further improve the performance of the proposed approach.

Super-Encryption Implementation Using Monoalphabetic Algorithm and XOR Algorithm for Data Security

NASA Astrophysics Data System (ADS)

Rachmawati, Dian; Andri Budiman, Mohammad; Aulia, Indra

2018-03-01

The exchange of data that occurs offline and online is very vulnerable to the threat of data theft. In general, cryptography is a science and art to maintain data secrecy. An encryption is a cryptography algorithm in which data is transformed into cipher text, which is something that is unreadable and meaningless so it cannot be read or understood by other parties. In super-encryption, two or more encryption algorithms are combined to make it more secure. In this work, Monoalphabetic algorithm and XOR algorithm are combined to form a super- encryption. Monoalphabetic algorithm works by changing a particular letter into a new letter based on existing keywords while the XOR algorithm works by using logic operation XOR Since Monoalphabetic algorithm is a classical cryptographic algorithm and XOR algorithm is a modern cryptographic algorithm, this scheme is expected to be both easy-to-implement and more secure. The combination of the two algorithms is capable of securing the data and restoring it back to its original form (plaintext), so the data integrity is still ensured.

Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.

PubMed

Hagita, Katsumi; Higuchi, Takeshi; Jinnai, Hiroshi

2018-04-12

Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

Smart sensors II; Proceedings of the Seminar, San Diego, CA, July 31, August 1, 1980

NASA Astrophysics Data System (ADS)

Barbe, D. F.

1980-01-01

Topics discussed include technology for smart sensors, smart sensors for tracking and surveillance, and techniques and algorithms for smart sensors. Papers are presented on the application of very large scale integrated circuits to smart sensors, imaging charge-coupled devices for deep-space surveillance, ultra-precise star tracking using charge coupled devices, and automatic target identification of blurred images with super-resolution features. Attention is also given to smart sensors for terminal homing, algorithms for estimating image position, and the computational efficiency of multiple image registration algorithms.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network

PubMed Central

Qu, Xiaobo; He, Yifan

2018-01-01

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods. PMID:29509666

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

PubMed

Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

2018-03-06

Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

Distance-based over-segmentation for single-frame RGB-D images

NASA Astrophysics Data System (ADS)

Fang, Zhuoqun; Wu, Chengdong; Chen, Dongyue; Jia, Tong; Yu, Xiaosheng; Zhang, Shihong; Qi, Erzhao

2017-11-01

Over-segmentation, known as super-pixels, is a widely used preprocessing step in segmentation algorithms. Oversegmentation algorithm segments an image into regions of perceptually similar pixels, but performs badly based on only color image in the indoor environments. Fortunately, RGB-D images can improve the performances on the images of indoor scene. In order to segment RGB-D images into super-pixels effectively, we propose a novel algorithm, DBOS (Distance-Based Over-Segmentation), which realizes full coverage of super-pixels on the image. DBOS fills the holes in depth images to fully utilize the depth information, and applies SLIC-like frameworks for fast running. Additionally, depth features such as plane projection distance are extracted to compute distance which is the core of SLIC-like frameworks. Experiments on RGB-D images of NYU Depth V2 dataset demonstrate that DBOS outperforms state-ofthe-art methods in quality while maintaining speeds comparable to them.

Super-resolution of fluorescence-free plasmonic nanoparticles using enhanced dark-field illumination based on wavelength-modulation

DOE PAGES

Zhang, Peng; Lee, Seungah; Yu, Hyunung; ...

2015-06-15

Super-resolution imaging of fluorescence-free plasmonic nanoparticles (NPs) was achieved using enhanced dark-field (EDF) illumination based on wavelength-modulation. Indistinguishable adjacent EDF images of 103-nm gold nanoparticles (GNPs), 40-nm gold nanorods (GNRs), and 80-nm silver nanoparticles (SNPs) were modulated at their wavelengths of specific localized surface plasmon scattering. The coordinates (x, y) of each NP were resolved by fitting their point spread functions with a two-dimensional Gaussian. The measured localization precisions of GNPs, GNRs, and SNPs were 2.5 nm, 5.0 nm, and 2.9 nm, respectively. From the resolved coordinates of NPs and the corresponding localization precisions, super-resolution images were reconstructed. Depending onmore » the spontaneous polarization of GNR scattering, the orientation angle of GNRs in two-dimensions was resolved and provided more elaborate localization information. This novel fluorescence-free super-resolution method was applied to live HeLa cells to resolve NPs and provided remarkable subdiffraction limit images.« less

Super-Chelators for Advanced Protein Labeling in Living Cells.

PubMed

Gatterdam, Karl; Joest, Eike F; Dietz, Marina S; Heilemann, Mike; Tampé, Robert

2018-05-14

Live-cell labeling, super-resolution microscopy, single-molecule applications, protein localization, or chemically induced assembly are emerging approaches, which require specific and very small interaction pairs. The minimal disturbance of protein function is essential to derive unbiased insights into cellular processes. Herein, we define a new class of hexavalent N-nitrilotriacetic acid (hexaNTA) chelators, displaying the highest affinity and stability of all NTA-based small interaction pairs described so far. Coupled to bright organic fluorophores with fine-tuned photophysical properties, the super-chelator probes were delivered into human cells by chemically gated nanopores. These super-chelators permit kinetic profiling, multiplexed labeling of His 6 - and His 12 -tagged proteins as well as single-molecule-based super-resolution imaging. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction.

PubMed

Yang, Qi; Zhang, Yanzhu; Zhao, Tiebiao; Chen, YangQuan

2017-04-04

Image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction aims to recover detailed information from low-resolution images and reconstruct them into high-resolution images. Due to the limited amount of data and information retrieved from low-resolution images, it is difficult to restore clear, artifact-free images, while still preserving enough structure of the image such as the texture. This paper presents a new single image super-resolution method which is based on adaptive fractional-order gradient interpolation and reconstruction. The interpolated image gradient via optimal fractional-order gradient is first constructed according to the image similarity and afterwards the minimum energy function is employed to reconstruct the final high-resolution image. Fractional-order gradient based interpolation methods provide an additional degree of freedom which helps optimize the implementation quality due to the fact that an extra free parameter α-order is being used. The proposed method is able to produce a rich texture detail while still being able to maintain structural similarity even under large zoom conditions. Experimental results show that the proposed method performs better than current single image super-resolution techniques. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Non-heuristic automatic techniques for overcoming low signal-to-noise-ratio bias of localization microscopy and multiple signal classification algorithm.

PubMed

Agarwal, Krishna; Macháň, Radek; Prasad, Dilip K

2018-03-21

Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples.

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

PubMed Central

Hu, Mao-Gui; Wang, Jin-Feng; Ge, Yong

2009-01-01

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intra-urban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolution-enhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well in detail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics. PMID:22291530

Super resolution imaging of HER2 gene amplification

NASA Astrophysics Data System (ADS)

Okada, Masaya; Kubo, Takuya; Masumoto, Kanako; Iwanaga, Shigeki

2016-02-01

HER2 positive breast cancer is currently examined by counting HER2 genes using fluorescence in situ hybridization (FISH)-stained breast carcinoma samples. In this research, two-dimensional super resolution fluorescence microscopy based on stochastic optical reconstruction microscopy (STORM), with a spatial resolution of approximately 20 nm in the lateral direction, was used to more precisely distinguish and count HER2 genes in a FISH-stained tissue section. Furthermore, by introducing double-helix point spread function (DH-PSF), an optical phase modulation technique, to super resolution microscopy, three-dimensional images were obtained of HER2 in a breast carcinoma sample approximately 4 μm thick.

Super-resolution optical telescopes with local light diffraction shrinkage

PubMed Central

Wang, Changtao; Tang, Dongliang; Wang, Yanqin; Zhao, Zeyu; Wang, Jiong; Pu, Mingbo; Zhang, Yudong; Yan, Wei; Gao, Ping; Luo, Xiangang

2015-01-01

Suffering from giant size of objective lenses and infeasible manipulations of distant targets, telescopes could not seek helps from present super-resolution imaging, such as scanning near-field optical microscopy, perfect lens and stimulated emission depletion microscopy. In this paper, local light diffraction shrinkage associated with optical super-oscillatory phenomenon is proposed for real-time and optically restoring super-resolution imaging information in a telescope system. It is found that fine target features concealed in diffraction-limited optical images of a telescope could be observed in a small local field of view, benefiting from a relayed metasurface-based super-oscillatory imaging optics in which some local Fourier components beyond the cut-off frequency of telescope could be restored. As experimental examples, a minimal resolution to 0.55 of Rayleigh criterion is obtained, and imaging complex targets and large targets by superimposing multiple local fields of views are demonstrated as well. This investigation provides an access for real-time, incoherent and super-resolution telescopes without the manipulation of distant targets. More importantly, it gives counterintuitive evidence to the common knowledge that relayed optics could not deliver more imaging details than objective systems. PMID:26677820

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution

PubMed Central

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-01-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images. PMID:29062159

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

PubMed

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Compact three-dimensional super-resolution system based on fluorescence emission difference microscopy

NASA Astrophysics Data System (ADS)

Zhu, Dazhao; Chen, Youhua; Fang, Yue; Hussain, Anwar; Kuang, Cuifang; Zhou, Xiaoxu; Xu, Yingke; Liu, Xu

2017-12-01

A compact microscope system for three-dimensional (3-D) super-resolution imaging is presented. The super-resolution capability of the system is based on a size-reduced effective 3-D point spread function generated through the fluorescence emission difference (FED) method. The appropriate polarization direction distribution and manipulation allows the panel active area of the spatial light modulator to be fully utilized. This allows simultaneous modulation of the incident light by two kinds of phase masks to be performed with a single spatial light modulator in order to generate a 3-D negative spot. The system is more compact than standard 3-D FED systems while maintaining all the advantages of 3-D FED microscopy. The experimental results demonstrated the improvement in 3-D resolution by nearly 1.7 times and 1.6 times compared to the classic confocal resolution in the lateral and axial directions, respectively.

Measuring true localization accuracy in super resolution microscopy with DNA-origami nanostructures

NASA Astrophysics Data System (ADS)

Reuss, Matthias; Fördős, Ferenc; Blom, Hans; Öktem, Ozan; Högberg, Björn; Brismar, Hjalmar

2017-02-01

A common method to assess the performance of (super resolution) microscopes is to use the localization precision of emitters as an estimate for the achieved resolution. Naturally, this is widely used in super resolution methods based on single molecule stochastic switching. This concept suffers from the fact that it is hard to calibrate measures against a real sample (a phantom), because true absolute positions of emitters are almost always unknown. For this reason, resolution estimates are potentially biased in an image since one is blind to true position accuracy, i.e. deviation in position measurement from true positions. We have solved this issue by imaging nanorods fabricated with DNA-origami. The nanorods used are designed to have emitters attached at each end in a well-defined and highly conserved distance. These structures are widely used to gauge localization precision. Here, we additionally determined the true achievable localization accuracy and compared this figure of merit to localization precision values for two common super resolution microscope methods STED and STORM.

Super-resolution links vinculin localization to function in focal adhesions.

PubMed

Giannone, Grégory

2015-07-01

Integrin-based focal adhesions integrate biochemical and biomechanical signals from the extracellular matrix and the actin cytoskeleton. The combination of three-dimensional super-resolution imaging and loss- or gain-of-function protein mutants now links the nanoscale dynamic localization of proteins to their activation and function within focal adhesions.

Portable microscopy platform for the clinical and environmental monitoring

NASA Astrophysics Data System (ADS)

Wang, Weiming; Yu, Yan; Huang, Hui; Ou, Jinping

2016-04-01

Light microscopy can not only address various diagnosis needs such as aquatic parasites and bacteria such as E. coli in water, but also provide a method for the screening of red tide. Traditional microscope based on the smartphone created by adding lens couldn't keep the tradeoff between field-of-view(FOV) and the resolution. In this paper, we demonstrate a non-contact, light and cost-effective microscope platform, that can image highly dense samples with a spatial resolution of ~0.8um over a field-of-view(FOV) of >1mm2. After captured the direct images, we performed the pixel super-resolution algorithm to improve the image resolution and overcome the hardware interference. The system would be a good point-of-care diagnostic solution in resource limited settings. We validated the performance of the system by imaging resolution test targets, the squamous cell cancer(SqCC) and green algae that necessary to detect the squamous carcinoma and red tide

Semantic super networks: A case analysis of Wikipedia papers

NASA Astrophysics Data System (ADS)

Kostyuchenko, Evgeny; Lebedeva, Taisiya; Goritov, Alexander

2017-11-01

An algorithm for constructing super-large semantic networks has been developed in current work. Algorithm was tested using the "Cosmos" category of the Internet encyclopedia "Wikipedia" as an example. During the implementation, a parser for the syntax analysis of Wikipedia pages was developed. A graph based on list of articles and categories was formed. On the basis of the obtained graph analysis, algorithms for finding domains of high connectivity in a graph were proposed and tested. Algorithms for constructing a domain based on the number of links and the number of articles in the current subject area is considered. The shortcomings of these algorithms are shown and explained, an algorithm is developed on their joint use. The possibility of applying a combined algorithm for obtaining the final domain is shown. The problem of instability of the received domain was discovered when starting an algorithm from two neighboring vertices related to the domain.

A Microfluidic Platform for Correlative Live-Cell and Super-Resolution Microscopy

PubMed Central

Tam, Johnny; Cordier, Guillaume Alan; Bálint, Štefan; Sandoval Álvarez, Ángel; Borbely, Joseph Steven; Lakadamyali, Melike

2014-01-01

Recently, super-resolution microscopy methods such as stochastic optical reconstruction microscopy (STORM) have enabled visualization of subcellular structures below the optical resolution limit. Due to the poor temporal resolution, however, these methods have mostly been used to image fixed cells or dynamic processes that evolve on slow time-scales. In particular, fast dynamic processes and their relationship to the underlying ultrastructure or nanoscale protein organization cannot be discerned. To overcome this limitation, we have recently developed a correlative and sequential imaging method that combines live-cell and super-resolution microscopy. This approach adds dynamic background to ultrastructural images providing a new dimension to the interpretation of super-resolution data. However, currently, it suffers from the need to carry out tedious steps of sample preparation manually. To alleviate this problem, we implemented a simple and versatile microfluidic platform that streamlines the sample preparation steps in between live-cell and super-resolution imaging. The platform is based on a microfluidic chip with parallel, miniaturized imaging chambers and an automated fluid-injection device, which delivers a precise amount of a specified reagent to the selected imaging chamber at a specific time within the experiment. We demonstrate that this system can be used for live-cell imaging, automated fixation, and immunostaining of adherent mammalian cells in situ followed by STORM imaging. We further demonstrate an application by correlating mitochondrial dynamics, morphology, and nanoscale mitochondrial protein distribution in live and super-resolution images. PMID:25545548

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Three-Dimensional Orientation of Anisotropic Plasmonic Aggregates at Intracellular Nuclear Indentation Sites by Integrated Light Sheet Super-Resolution Microscopy.

PubMed

Chakkarapani, Suresh Kumar; Sun, Yucheng; Lee, Seungah; Fang, Ning; Kang, Seong Ho

2018-05-22

Three-dimensional (3D) orientations of individual anisotropic plasmonic nanoparticles in aggregates were observed in real time by integrated light sheet super-resolution microscopy ( iLSRM). Asymmetric light scattering of a gold nanorod (AuNR) was used to trigger signals based on the polarizer angle. Controlled photoswitching was achieved by turning the polarizer and obtaining a series of images at different polarization directions. 3D subdiffraction-limited super-resolution images were obtained by superlocalization of scattering signals as a function of the anisotropic optical properties of AuNRs. Varying the polarizer angle allowed resolution of the orientation of individual AuNRs. 3D images of individual nanoparticles were resolved in aggregated regions, resulting in as low as 64 nm axial resolution and 28 nm spatial resolution. The proposed imaging setup and localization approach demonstrates a convenient method for imaging under a noisy environment where the majority of scattering noise comes from cellular components. This integrated 3D iLSRM and localization technique was shown to be reliable and useful in the field of 3D nonfluorescence super-resolution imaging.

A robust statistical estimation (RoSE) algorithm jointly recovers the 3D location and intensity of single molecules accurately and precisely

NASA Astrophysics Data System (ADS)

Mazidi, Hesam; Nehorai, Arye; Lew, Matthew D.

2018-02-01

In single-molecule (SM) super-resolution microscopy, the complexity of a biological structure, high molecular density, and a low signal-to-background ratio (SBR) may lead to imaging artifacts without a robust localization algorithm. Moreover, engineered point spread functions (PSFs) for 3D imaging pose difficulties due to their intricate features. We develop a Robust Statistical Estimation algorithm, called RoSE, that enables joint estimation of the 3D location and photon counts of SMs accurately and precisely using various PSFs under conditions of high molecular density and low SBR.

Super-Resolution Optical Fluctuation Bio-Imaging with Dual-Color Carbon Nanodots.

PubMed

Chizhik, Anna M; Stein, Simon; Dekaliuk, Mariia O; Battle, Christopher; Li, Weixing; Huss, Anja; Platen, Mitja; Schaap, Iwan A T; Gregor, Ingo; Demchenko, Alexander P; Schmidt, Christoph F; Enderlein, Jörg; Chizhik, Alexey I

2016-01-13

Success in super-resolution imaging relies on a proper choice of fluorescent probes. Here, we suggest novel easily produced and biocompatible nanoparticles-carbon nanodots-for super-resolution optical fluctuation bioimaging (SOFI). The particles revealed an intrinsic dual-color fluorescence, which corresponds to two subpopulations of particles of different electric charges. The neutral nanoparticles localize to cellular nuclei suggesting their potential use as an inexpensive, easily produced nucleus-specific label. The single particle study revealed that the carbon nanodots possess a unique hybrid combination of fluorescence properties exhibiting characteristics of both dye molecules and semiconductor nanocrystals. The results suggest that charge trapping and redistribution on the surface of the particles triggers their transitions between emissive and dark states. These findings open up new possibilities for the utilization of carbon nanodots in the various super-resolution microscopy methods based on stochastic optical switching.

On the dynamic readout characteristic of nonlinear super-resolution optical storage

NASA Astrophysics Data System (ADS)

Wei, Jingsong

2013-03-01

Researchers have developed nonlinear super-resolution optical storage for the past twenty years. However, several concerns remain, including (1) the presence of readout threshold power; (2) the increase of threshold power with the reduction of the mark size, and (3) the increase of the carrier-to-noise ratio (CNR) at the initial stage and then decrease with the increase of readout laser power or laser irradiation time. The present work calculates and analyzes the super-resolution spot formed by the thin film masks and the readout threshold power characteristic according to the derived formula and based on the nonlinear saturable absorption characteristic and threshold of structural change. The obtained theoretical calculation and experimental data answer the concerns regarding the dynamic readout threshold characteristic and CNR dependence on laser power and irradiation time. The near-field optical spot scanning experiment further verifies the super-resolution spot formation produced through the nonlinear thin film masks.

Fundamental techniques for resolution enhancement of average subsampled images

NASA Astrophysics Data System (ADS)

Shen, Day-Fann; Chiu, Chui-Wen

2012-07-01

Although single image resolution enhancement, otherwise known as super-resolution, is widely regarded as an ill-posed inverse problem, we re-examine the fundamental relationship between a high-resolution (HR) image acquisition module and its low-resolution (LR) counterpart. Analysis shows that partial HR information is attenuated but still exists, in its LR version, through the fundamental averaging-and-subsampling process. As a result, we propose a modified Laplacian filter (MLF) and an intensity correction process (ICP) as the pre and post process, respectively, with an interpolation algorithm to partially restore the attenuated information in a super-resolution (SR) enhanced image image. Experiments show that the proposed MLF and ICP provide significant and consistent quality improvements on all 10 test images with three well known interpolation methods including bilinear, bi-cubic, and the SR graphical user interface program provided by Ecole Polytechnique Federale de Lausanne. The proposed MLF and ICP are simple in implementation and generally applicable to all average-subsampled LR images. MLF and ICP, separately or together, can be integrated into most interpolation methods that attempt to restore the original HR contents. Finally, the idea of MLF and ICP can also be applied for average, subsampled one-dimensional signal.

Dual Super-Systolic Core for Real-Time Reconstructive Algorithms of High-Resolution Radar/SAR Imaging Systems

PubMed Central

Atoche, Alejandro Castillo; Castillo, Javier Vázquez

2012-01-01

A high-speed dual super-systolic core for reconstructive signal processing (SP) operations consists of a double parallel systolic array (SA) machine in which each processing element of the array is also conceptualized as another SA in a bit-level fashion. In this study, we addressed the design of a high-speed dual super-systolic array (SSA) core for the enhancement/reconstruction of remote sensing (RS) imaging of radar/synthetic aperture radar (SAR) sensor systems. The selected reconstructive SP algorithms are efficiently transformed in their parallel representation and then, they are mapped into an efficient high performance embedded computing (HPEC) architecture in reconfigurable Xilinx field programmable gate array (FPGA) platforms. As an implementation test case, the proposed approach was aggregated in a HW/SW co-design scheme in order to solve the nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) from a remotely sensed scene. We show how such dual SSA core, drastically reduces the computational load of complex RS regularization techniques achieving the required real-time operational mode. PMID:22736964

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

DMD-based LED-illumination super-resolution and optical sectioning microscopy.

PubMed

Dan, Dan; Lei, Ming; Yao, Baoli; Wang, Wen; Winterhalder, Martin; Zumbusch, Andreas; Qi, Yujiao; Xia, Liang; Yan, Shaohui; Yang, Yanlong; Gao, Peng; Ye, Tong; Zhao, Wei

2013-01-01

Super-resolution three-dimensional (3D) optical microscopy has incomparable advantages over other high-resolution microscopic technologies, such as electron microscopy and atomic force microscopy, in the study of biological molecules, pathways and events in live cells and tissues. We present a novel approach of structured illumination microscopy (SIM) by using a digital micromirror device (DMD) for fringe projection and a low-coherence LED light for illumination. The lateral resolution of 90 nm and the optical sectioning depth of 120 μm were achieved. The maximum acquisition speed for 3D imaging in the optical sectioning mode was 1.6×10(7) pixels/second, which was mainly limited by the sensitivity and speed of the CCD camera. In contrast to other SIM techniques, the DMD-based LED-illumination SIM is cost-effective, ease of multi-wavelength switchable and speckle-noise-free. The 2D super-resolution and 3D optical sectioning modalities can be easily switched and applied to either fluorescent or non-fluorescent specimens.

DMD-based LED-illumination Super-resolution and optical sectioning microscopy

PubMed Central

Dan, Dan; Lei, Ming; Yao, Baoli; Wang, Wen; Winterhalder, Martin; Zumbusch, Andreas; Qi, Yujiao; Xia, Liang; Yan, Shaohui; Yang, Yanlong; Gao, Peng; Ye, Tong; Zhao, Wei

2013-01-01

Super-resolution three-dimensional (3D) optical microscopy has incomparable advantages over other high-resolution microscopic technologies, such as electron microscopy and atomic force microscopy, in the study of biological molecules, pathways and events in live cells and tissues. We present a novel approach of structured illumination microscopy (SIM) by using a digital micromirror device (DMD) for fringe projection and a low-coherence LED light for illumination. The lateral resolution of 90 nm and the optical sectioning depth of 120 μm were achieved. The maximum acquisition speed for 3D imaging in the optical sectioning mode was 1.6×107 pixels/second, which was mainly limited by the sensitivity and speed of the CCD camera. In contrast to other SIM techniques, the DMD-based LED-illumination SIM is cost-effective, ease of multi-wavelength switchable and speckle-noise-free. The 2D super-resolution and 3D optical sectioning modalities can be easily switched and applied to either fluorescent or non-fluorescent specimens. PMID:23346373

Improved localization accuracy in stochastic super-resolution fluorescence microscopy by K-factor image deshadowing

PubMed Central

Ilovitsh, Tali; Meiri, Amihai; Ebeling, Carl G.; Menon, Rajesh; Gerton, Jordan M.; Jorgensen, Erik M.; Zalevsky, Zeev

2013-01-01

Localization of a single fluorescent particle with sub-diffraction-limit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same super-resolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution. PMID:24466491

Depletion-based techniques for super-resolution imaging of NV-diamond

NASA Astrophysics Data System (ADS)

Jaskula, Jean-Christophe; Trifonov, Alexei; Glenn, David; Walsworth, Ronald

2012-06-01

We discuss the development and application of depletion-based techniques for super-resolution imaging of NV centers in diamond: stimulated emission depletion (STED), metastable ground state depletion (GSD), and dark state depletion (DSD). NV centers in diamond do not bleach under optical excitation, are not biotoxic, and have long-lived electronic spin coherence and spin-state-dependent fluorescence. Thus NV-diamond has great potential as a fluorescent biomarker and as a magnetic biosensor.

Sensorless adaptive optics for isoSTED nanoscopy

NASA Astrophysics Data System (ADS)

Antonello, Jacopo; Hao, Xiang; Allgeyer, Edward S.; Bewersdorf, Joerg; Rittscher, Jens; Booth, Martin J.

2018-02-01

The presence of aberrations is a major concern when using fluorescence microscopy to image deep inside tissue. Aberrations due to refractive index mismatch and heterogeneity of the specimen under investigation cause severe reduction in the amount of fluorescence emission that is collected by the microscope. Furthermore, aberrations adversely affect the resolution, leading to loss of fine detail in the acquired images. These phenomena are particularly troublesome for super-resolution microscopy techniques such as isotropic stimulated-emission-depletion microscopy (isoSTED), which relies on accurate control of the shape and co-alignment of multiple excitation and depletion foci to operate as expected and to achieve the super-resolution effect. Aberrations can be suppressed by implementing sensorless adaptive optics techniques, whereby aberration correction is achieved by maximising a certain image quality metric. In confocal microscopy for example, one can employ the total image brightness as an image quality metric. Aberration correction is subsequently achieved by iteratively changing the settings of a wavefront corrector device until the metric is maximised. This simplistic approach has limited applicability to isoSTED microscopy where, due to the complex interplay between the excitation and depletion foci, maximising the total image brightness can lead to introducing aberrations in the depletion foci. In this work we first consider the effects that different aberration modes have on isoSTED microscopes. We then propose an iterative, wavelet-based aberration correction algorithm and evaluate its benefits.

Aberrations and adaptive optics in super-resolution microscopy

PubMed Central

Booth, Martin; Andrade, Débora; Burke, Daniel; Patton, Brian; Zurauskas, Mantas

2015-01-01

As one of the most powerful tools in the biological investigation of cellular structures and dynamic processes, fluorescence microscopy has undergone extraordinary developments in the past decades. The advent of super-resolution techniques has enabled fluorescence microscopy – or rather nanoscopy – to achieve nanoscale resolution in living specimens and unravelled the interior of cells with unprecedented detail. The methods employed in this expanding field of microscopy, however, are especially prone to the detrimental effects of optical aberrations. In this review, we discuss how super-resolution microscopy techniques based upon single-molecule switching, stimulated emission depletion and structured illumination each suffer from aberrations in different ways that are dependent upon intrinsic technical aspects. We discuss the use of adaptive optics as an effective means to overcome this problem. PMID:26124194

Conical diffraction as a versatile building block to implement new imaging modalities for superresolution in fluorescence microscopy

NASA Astrophysics Data System (ADS)

Fallet, Clément; Caron, Julien; Oddos, Stephane; Tinevez, Jean-Yves; Moisan, Lionel; Sirat, Gabriel Y.; Braitbart, Philippe O.; Shorte, Spencer L.

2014-08-01

We present a new technology for super-resolution fluorescence imaging, based on conical diffraction. Conical diffraction is a linear, singular phenomenon taking place when a polarized beam is diffracted through a biaxial crystal. The illumination patterns generated by conical diffraction are more compact than the classical Gaussian beam; we use them to generate a super-resolution imaging modality. Conical Diffraction Microscopy (CODIM) resolution enhancement can be achieved with any type of objective on any kind of sample preparation and standard fluorophores. Conical diffraction can be used in multiple fashion to create new and disruptive technologies for super-resolution microscopy. This paper will focus on the first one that has been implemented and give a glimpse at what the future of microscopy using conical diffraction could be.

Super-resolution using a light inception layer in convolutional neural network

NASA Astrophysics Data System (ADS)

Mou, Qinyang; Guo, Jun

2018-04-01

Recently, several models based on CNN architecture have achieved great result on Single Image Super-Resolution (SISR) problem. In this paper, we propose an image super-resolution method (SR) using a light inception layer in convolutional network (LICN). Due to the strong representation ability of our well-designed inception layer that can learn richer representation with less parameters, we can build our model with shallow architecture that can reduce the effect of vanishing gradients problem and save computational costs. Our model strike a balance between computational speed and the quality of the result. Compared with state-of-the-art result, we produce comparable or better results with faster computational speed.

Towards real-time image deconvolution: application to confocal and STED microscopy

PubMed Central

Zanella, R.; Zanghirati, G.; Cavicchioli, R.; Zanni, L.; Boccacci, P.; Bertero, M.; Vicidomini, G.

2013-01-01

Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic processing units (GPUs). We test the proposed algorithms both on synthetic and real data of confocal and STED microscopy. Combining the SGP method with the GPU implementation we achieve a speed-up factor from about a factor 25 to 690 (with respect the conventional algorithm). The excellent results obtained on STED microscopy images demonstrate the synergy between super-resolution techniques and image-deconvolution. Further, the real-time processing allows conserving one of the most important property of STED microscopy, i.e the ability to provide fast sub-diffraction resolution recordings. PMID:23982127

Three-dimensional nanometre localization of nanoparticles to enhance super-resolution microscopy

NASA Astrophysics Data System (ADS)

Bon, Pierre; Bourg, Nicolas; Lécart, Sandrine; Monneret, Serge; Fort, Emmanuel; Wenger, Jérôme; Lévêque-Fort, Sandrine

2015-07-01

Meeting the nanometre resolution promised by super-resolution microscopy techniques (pointillist: PALM, STORM, scanning: STED) requires stabilizing the sample drifts in real time during the whole acquisition process. Metal nanoparticles are excellent probes to track the lateral drifts as they provide crisp and photostable information. However, achieving nanometre axial super-localization is still a major challenge, as diffraction imposes large depths-of-fields. Here we demonstrate fast full three-dimensional nanometre super-localization of gold nanoparticles through simultaneous intensity and phase imaging with a wavefront-sensing camera based on quadriwave lateral shearing interferometry. We show how to combine the intensity and phase information to provide the key to the third axial dimension. Presently, we demonstrate even in the occurrence of large three-dimensional fluctuations of several microns, unprecedented sub-nanometre localization accuracies down to 0.7 nm in lateral and 2.7 nm in axial directions at 50 frames per second. We demonstrate that nanoscale stabilization greatly enhances the image quality and resolution in direct stochastic optical reconstruction microscopy imaging.

Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy.

PubMed

Traenkle, Bjoern; Rothbauer, Ulrich

2017-01-01

Single-domain antibodies (sdAbs) have substantially expanded the possibilities of advanced cellular imaging such as live-cell or super-resolution microscopy to visualize cellular antigens and their dynamics. In addition to their unique properties including small size, high stability, and solubility in many environments, sdAbs can be efficiently functionalized according to the needs of the respective imaging approach. Genetically encoded intrabodies fused to fluorescent proteins (chromobodies) have become versatile tools to study dynamics of endogenous proteins in living cells. Additionally, sdAbs conjugated to organic dyes were shown to label cellular structures with high density and minimal fluorophore displacement making them highly attractive probes for super-resolution microscopy. Here, we review recent advances of the chromobody technology to visualize localization and dynamics of cellular targets and the application of chromobody-based cell models for compound screening. Acknowledging the emerging importance of super-resolution microscopy in cell biology, we further discuss advantages and challenges of sdAbs for this technology.

Newmark-Beta-FDTD method for super-resolution analysis of time reversal waves

NASA Astrophysics Data System (ADS)

Shi, Sheng-Bing; Shao, Wei; Ma, Jing; Jin, Congjun; Wang, Xiao-Hua

2017-09-01

In this work, a new unconditionally stable finite-difference time-domain (FDTD) method with the split-field perfectly matched layer (PML) is proposed for the analysis of time reversal (TR) waves. The proposed method is very suitable for multiscale problems involving microstructures. The spatial and temporal derivatives in this method are discretized by the central difference technique and Newmark-Beta algorithm, respectively, and the derivation results in the calculation of a banded-sparse matrix equation. Since the coefficient matrix keeps unchanged during the whole simulation process, the lower-upper (LU) decomposition of the matrix needs to be performed only once at the beginning of the calculation. Moreover, the reverse Cuthill-Mckee (RCM) technique, an effective preprocessing technique in bandwidth compression of sparse matrices, is used to improve computational efficiency. The super-resolution focusing of TR wave propagation in two- and three-dimensional spaces is included to validate the accuracy and efficiency of the proposed method.

Interrogating Surface Functional Group Heterogeneity of Activated Thermoplastics Using Super-Resolution Fluorescence Microscopy.

PubMed

ONeil, Colleen E; Jackson, Joshua M; Shim, Sang-Hee; Soper, Steven A

2016-04-05

We present a novel approach for characterizing surfaces utilizing super-resolution fluorescence microscopy with subdiffraction limit spatial resolution. Thermoplastic surfaces were activated by UV/O3 or O2 plasma treatment under various conditions to generate pendant surface-confined carboxylic acids (-COOH). These surface functional groups were then labeled with a photoswitchable dye and interrogated using single-molecule, localization-based, super-resolution fluorescence microscopy to elucidate the surface heterogeneity of these functional groups across the activated surface. Data indicated nonuniform distributions of these functional groups for both COC and PMMA thermoplastics with the degree of heterogeneity being dose dependent. In addition, COC demonstrated relative higher surface density of functional groups compared to PMMA for both UV/O3 and O2 plasma treatment. The spatial distribution of -COOH groups secured from super-resolution imaging were used to simulate nonuniform patterns of electroosmotic flow in thermoplastic nanochannels. Simulations were compared to single-particle tracking of fluorescent nanoparticles within thermoplastic nanoslits to demonstrate the effects of surface functional group heterogeneity on the electrokinetic transport process.

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Super resolution reconstruction of μ-CT image of rock sample using neighbour embedding algorithm

NASA Astrophysics Data System (ADS)

Wang, Yuzhu; Rahman, Sheik S.; Arns, Christoph H.

2018-03-01

X-ray computed tomography (μ-CT) is considered to be the most effective way to obtain the inner structure of rock sample without destructions. However, its limited resolution hampers its ability to probe sub-micro structures which is critical for flow transportation of rock sample. In this study, we propose an innovative methodology to improve the resolution of μ-CT image using neighbour embedding algorithm where low frequency information is provided by μ-CT image itself while high frequency information is supplemented by high resolution scanning electron microscopy (SEM) image. In order to obtain prior for reconstruction, a large number of image patch pairs contain high- and low- image patches are extracted from the Gaussian image pyramid generated by SEM image. These image patch pairs contain abundant information about tomographic evolution of local porous structures under different resolution spaces. Relying on the assumption of self-similarity of porous structure, this prior information can be used to supervise the reconstruction of high resolution μ-CT image effectively. The experimental results show that the proposed method is able to achieve the state-of-the-art performance.

Multicolor Super-Resolution Fluorescence Imaging via Multi-Parameter Fluorophore Detection

PubMed Central

Bates, Mark; Dempsey, Graham T; Chen, Kok Hao; Zhuang, Xiaowei

2012-01-01

Understanding the complexity of the cellular environment will benefit from the ability to unambiguously resolve multiple cellular components, simultaneously and with nanometer-scale spatial resolution. Multicolor super-resolution fluorescence microscopy techniques have been developed to achieve this goal, yet challenges remain in terms of the number of targets that can be simultaneously imaged and the crosstalk between color channels. Herein, we demonstrate multicolor stochastic optical reconstruction microscopy (STORM) based on a multi-parameter detection strategy, which uses both the fluorescence activation wavelength and the emission color to discriminate between photo-activatable fluorescent probes. First, we obtained two-color super-resolution images using the near-infrared cyanine dye Alexa 750 in conjunction with a red cyanine dye Alexa 647, and quantified color crosstalk levels and image registration accuracy. Combinatorial pairing of these two switchable dyes with fluorophores which enhance photo-activation enabled multi-parameter detection of six different probes. Using this approach, we obtained six-color super-resolution fluorescence images of a model sample. The combination of multiple fluorescence detection parameters for improved fluorophore discrimination promises to substantially enhance our ability to visualize multiple cellular targets with sub-diffraction-limit resolution. PMID:22213647

Estimation of a super-resolved PSF for the data reduction of undersampled stellar observations. Deriving an accurate model for fitting photometry with Corot space telescope

NASA Astrophysics Data System (ADS)

Pinheiro da Silva, L.; Auvergne, M.; Toublanc, D.; Rowe, J.; Kuschnig, R.; Matthews, J.

2006-06-01

Context: .Fitting photometry algorithms can be very effective provided that an accurate model of the instrumental point spread function (PSF) is available. When high-precision time-resolved photometry is required, however, the use of point-source star images as empirical PSF models can be unsatisfactory, due to the limits in their spatial resolution. Theoretically-derived models, on the other hand, are limited by the unavoidable assumption of simplifying hypothesis, while the use of analytical approximations is restricted to regularly-shaped PSFs. Aims: .This work investigates an innovative technique for space-based fitting photometry, based on the reconstruction of an empirical but properly-resolved PSF. The aim is the exploitation of arbitrary star images, including those produced under intentional defocus. The cases of both MOST and COROT, the first space telescopes dedicated to time-resolved stellar photometry, are considered in the evaluation of the effectiveness and performances of the proposed methodology. Methods: .PSF reconstruction is based on a set of star images, periodically acquired and presenting relative subpixel displacements due to motion of the acquisition system, in this case the jitter of the satellite attitude. Higher resolution is achieved through the solution of the inverse problem. The approach can be regarded as a special application of super-resolution techniques, though a specialised procedure is proposed to better meet the PSF determination problem specificities. The application of such a model to fitting photometry is illustrated by numerical simulations for COROT and on a complete set of observations from MOST. Results: .We verify that, in both scenarios, significantly better resolved PSFs can be estimated, leading to corresponding improvements in photometric results. For COROT, indeed, subpixel reconstruction enabled the successful use of fitting algorithms despite its rather complex PSF profile, which could hardly be modeled otherwise. For MOST, whose direct-imaging PSF is closer to the ordinary, comparison to other models or photometry techniques were carried out and confirmed the potential of PSF reconstruction in real observational conditions.

Polarization sensitive localization based super-resolution microscopy with a birefringent wedge

NASA Astrophysics Data System (ADS)

Sinkó, József; Gajdos, Tamás; Czvik, Elvira; Szabó, Gábor; Erdélyi, Miklós

2017-03-01

A practical method has been presented for polarization sensitive localization based super-resolution microscopy using a birefringent dual wedge. The measurement of the polarization degree at the single molecule level can reveal the chemical and physical properties of the local environment of the fluorescent dye molecule and can hence provide information about the sub-diffraction sized structure of biological samples. Polarization sensitive STORM imaging of the F-Actins proved correlation between the orientation of fluorescent dipoles and the axis of the fibril.

Development of Super-Ensemble techniques for ocean analyses: the Mediterranean Sea case

NASA Astrophysics Data System (ADS)

Pistoia, Jenny; Pinardi, Nadia; Oddo, Paolo; Collins, Matthew; Korres, Gerasimos; Drillet, Yann

2017-04-01

Short-term ocean analyses for Sea Surface Temperature SST in the Mediterranean Sea can be improved by a statistical post-processing technique, called super-ensemble. This technique consists in a multi-linear regression algorithm applied to a Multi-Physics Multi-Model Super-Ensemble (MMSE) dataset, a collection of different operational forecasting analyses together with ad-hoc simulations produced by modifying selected numerical model parameterizations. A new linear regression algorithm based on Empirical Orthogonal Function filtering techniques is capable to prevent overfitting problems, even if best performances are achieved when we add correlation to the super-ensemble structure using a simple spatial filter applied after the linear regression. Our outcomes show that super-ensemble performances depend on the selection of an unbiased operator and the length of the learning period, but the quality of the generating MMSE dataset has the largest impact on the MMSE analysis Root Mean Square Error (RMSE) evaluated with respect to observed satellite SST. Lower RMSE analysis estimates result from the following choices: 15 days training period, an overconfident MMSE dataset (a subset with the higher quality ensemble members), and the least square algorithm being filtered a posteriori.

Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data.

PubMed

Veganzones, Miguel A; Simoes, Miguel; Licciardi, Giorgio; Yokoya, Naoto; Bioucas-Dias, Jose M; Chanussot, Jocelyn

2016-01-01

Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.

Aberrations and adaptive optics in super-resolution microscopy.

PubMed

Booth, Martin; Andrade, Débora; Burke, Daniel; Patton, Brian; Zurauskas, Mantas

2015-08-01

As one of the most powerful tools in the biological investigation of cellular structures and dynamic processes, fluorescence microscopy has undergone extraordinary developments in the past decades. The advent of super-resolution techniques has enabled fluorescence microscopy - or rather nanoscopy - to achieve nanoscale resolution in living specimens and unravelled the interior of cells with unprecedented detail. The methods employed in this expanding field of microscopy, however, are especially prone to the detrimental effects of optical aberrations. In this review, we discuss how super-resolution microscopy techniques based upon single-molecule switching, stimulated emission depletion and structured illumination each suffer from aberrations in different ways that are dependent upon intrinsic technical aspects. We discuss the use of adaptive optics as an effective means to overcome this problem. © The Author 2015. Published by Oxford University Press on behalf of The Japanese Society of Microscopy.

Vernier-like super resolution with guided correlated photon pairs.

PubMed

Nespoli, Matteo; Goan, Hsi-Sheng; Shih, Min-Hsiung

2016-01-11

We describe a dispersion-enabled, ultra-low power realization of super-resolution in an integrated Mach-Zehnder interferometer. Our scheme is based on a Vernier-like effect in the coincident detection of frequency correlated, non-degenerate photon pairs at the sensor output in the presence of group index dispersion. We design and simulate a realistic integrated refractive index sensor in a silicon nitride on silica platform and characterize its performance in the proposed scheme. We present numerical results showing a sensitivity improvement upward of 40 times over a traditional sensing scheme. The device we design is well within the reach of modern semiconductor fabrication technology. We believe this is the first metrology scheme that uses waveguide group index dispersion as a resource to attain super-resolution.

Plasmonics and metamaterials based super-resolution imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Liu, Zhaowei

2017-05-01

In recent years, surface imaging of various biological dynamics and biomechanical phenomena has seen a surge of interest. Imaging of processes such as exocytosis and kinesin motion are most effective when depth is limited to a very thin region of interest at the edge of the cell or specimen. However, many objects and processes of interest are of size scales below the diffraction limit for safe, visible wavelength illumination. Super-resolution imaging methods such as structured illumination microscopy and others have offered various compromises between resolution, imaging speed, and bio-compatibility. In this talk, I will present our most recent progress in plasmonic structured illumination microscopy (PSIM) and localized plasmonic structured illumination microscopy (LPSIM), and their applications in bio-imaging. We have achieved wide-field surface imaging with resolution down to 75 nm while maintaining reasonable speed and compatibility with biological specimens. These plasmonic enhanced super resolution techniques offer unique solutions to obtain 50nm spatial resolution and 50 frames per second wide imaging speed at the same time.

Combined multi-plane phase retrieval and super-resolution optical fluctuation imaging for 4D cell microscopy

NASA Astrophysics Data System (ADS)

Descloux, A.; Grußmayer, K. S.; Bostan, E.; Lukes, T.; Bouwens, A.; Sharipov, A.; Geissbuehler, S.; Mahul-Mellier, A.-L.; Lashuel, H. A.; Leutenegger, M.; Lasser, T.

2018-03-01

Super-resolution fluorescence microscopy provides unprecedented insight into cellular and subcellular structures. However, going `beyond the diffraction barrier' comes at a price, since most far-field super-resolution imaging techniques trade temporal for spatial super-resolution. We propose the combination of a novel label-free white light quantitative phase imaging with fluorescence to provide high-speed imaging and spatial super-resolution. The non-iterative phase retrieval relies on the acquisition of single images at each z-location and thus enables straightforward 3D phase imaging using a classical microscope. We realized multi-plane imaging using a customized prism for the simultaneous acquisition of eight planes. This allowed us to not only image live cells in 3D at up to 200 Hz, but also to integrate fluorescence super-resolution optical fluctuation imaging within the same optical instrument. The 4D microscope platform unifies the sensitivity and high temporal resolution of phase imaging with the specificity and high spatial resolution of fluorescence microscopy.

Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations

PubMed Central

Gustafsson, Nils; Culley, Siân; Ashdown, George; Owen, Dylan M.; Pereira, Pedro Matos; Henriques, Ricardo

2016-01-01

Despite significant progress, high-speed live-cell super-resolution studies remain limited to specialized optical setups, generally requiring intense phototoxic illumination. Here, we describe a new analytical approach, super-resolution radial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin. In the most challenging data sets for super-resolution, such as those obtained in low-illumination live-cell imaging with GFP, we show that SRRF is generally capable of achieving resolutions better than 150 nm. Meanwhile, for data sets similar to those obtained in PALM or STORM imaging, SRRF achieves resolutions approaching those of standard single-molecule localization analysis. The broad applicability of SRRF and its performance at low signal-to-noise ratios allows super-resolution using modern widefield, confocal or TIRF microscopes with illumination orders of magnitude lower than methods such as PALM, STORM or STED. We demonstrate this by super-resolution live-cell imaging over timescales ranging from minutes to hours. PMID:27514992

Sparsity-Based Super Resolution for SEM Images.

PubMed

Tsiper, Shahar; Dicker, Or; Kaizerman, Idan; Zohar, Zeev; Segev, Mordechai; Eldar, Yonina C

2017-09-13

The scanning electron microscope (SEM) is an electron microscope that produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is formed. Since its invention in 1942, the capabilities of SEMs have become paramount in the discovery and understanding of the nanometer world, and today it is extensively used for both research and in industry. In principle, SEMs can achieve resolution better than one nanometer. However, for many applications, working at subnanometer resolution implies an exceedingly large number of scanning points. For exactly this reason, the SEM diagnostics of microelectronic chips is performed either at high resolution (HR) over a small area or at low resolution (LR) while capturing a larger portion of the chip. Here, we employ sparse coding and dictionary learning to algorithmically enhance low-resolution SEM images of microelectronic chips-up to the level of the HR images acquired by slow SEM scans, while considerably reducing the noise. Our methodology consists of two steps: an offline stage of learning a joint dictionary from a sequence of LR and HR images of the same region in the chip, followed by a fast-online super-resolution step where the resolution of a new LR image is enhanced. We provide several examples with typical chips used in the microelectronics industry, as well as a statistical study on arbitrary images with characteristic structural features. Conceptually, our method works well when the images have similar characteristics, as microelectronics chips do. This work demonstrates that employing sparsity concepts can greatly improve the performance of SEM, thereby considerably increasing the scanning throughput without compromising on analysis quality and resolution.

Super-resolution photoacoustic microscopy using joint sparsity

NASA Astrophysics Data System (ADS)

Burgholzer, P.; Haltmeier, M.; Berer, T.; Leiss-Holzinger, E.; Murray, T. W.

2017-07-01

We present an imaging method that uses the random optical speckle patterns that naturally emerge as light propagates through strongly scattering media as a structured illumination source for photoacoustic imaging. Our approach, termed blind structured illumination photoacoustic microscopy (BSIPAM), was inspired by recent work in fluorescence microscopy where super-resolution imaging was demonstrated using multiple unknown speckle illumination patterns. We extend this concept to the multiple scattering domain using photoacoustics (PA), with the speckle pattern serving to generate ultrasound. The optical speckle pattern that emerges as light propagates through diffuse media provides structured illumination to an object placed behind a scattering wall. The photoacoustic signal produced by such illumination is detected using a focused ultrasound transducer. We demonstrate through both simulation and experiment, that by acquiring multiple photoacoustic images, each produced by a different random and unknown speckle pattern, an image of an absorbing object can be reconstructed with a spatial resolution far exceeding that of the ultrasound transducer. We experimentally and numerically demonstrate a gain in resolution of more than a factor of two by using multiple speckle illuminations. The variations in the photoacoustic signals generated with random speckle patterns are utilized in BSIPAM using a novel reconstruction algorithm. Exploiting joint sparsity, this algorithm is capable of reconstructing the absorbing structure from measured PA signals with a resolution close to the speckle size. Another way to excite random excitation for photoacoustic imaging are small absorbing particles, including contrast agents, which flow through small vessels. For such a set-up, the joint-sparsity is generated by the fact that all the particles move in the same vessels. Structured illumination in that case is not necessary.

A Microfluidic Cytometer for Complete Blood Count With a 3.2-Megapixel, 1.1- μm-Pitch Super-Resolution Image Sensor in 65-nm BSI CMOS.

PubMed

Liu, Xu; Huang, Xiwei; Jiang, Yu; Xu, Hang; Guo, Jing; Hou, Han Wei; Yan, Mei; Yu, Hao

2017-08-01

Based on a 3.2-Megapixel 1.1- μm-pitch super-resolution (SR) CMOS image sensor in a 65-nm backside-illumination process, a lens-free microfluidic cytometer for complete blood count (CBC) is demonstrated in this paper. Backside-illumination improves resolution and contrast at the device level with elimination of surface treatment when integrated with microfluidic channels. A single-frame machine-learning-based SR processing is further realized at system level for resolution correction with minimum hardware resources. The demonstrated microfluidic cytometer can detect the platelet cells (< 2 μm) required in CBC, hence is promising for point-of-care diagnostics.

CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography).

PubMed

Siegel, Nisan; Storrie, Brian; Bruce, Marc; Brooker, Gary

2015-02-07

FINCH holographic fluorescence microscopy creates high resolution super-resolved images with enhanced depth of focus. The simple addition of a real-time Nipkow disk confocal image scanner in a conjugate plane of this incoherent holographic system is shown to reduce the depth of focus, and the combination of both techniques provides a simple way to enhance the axial resolution of FINCH in a combined method called "CINCH". An important feature of the combined system allows for the simultaneous real-time image capture of widefield and holographic images or confocal and confocal holographic images for ready comparison of each method on the exact same field of view. Additional GPU based complex deconvolution processing of the images further enhances resolution.

Video-rate nanoscopy enabled by sCMOS camera-specific single-molecule localization algorithms

PubMed Central

Huang, Fang; Hartwich, Tobias M. P.; Rivera-Molina, Felix E.; Lin, Yu; Duim, Whitney C.; Long, Jane J.; Uchil, Pradeep D.; Myers, Jordan R.; Baird, Michelle A.; Mothes, Walther; Davidson, Michael W.; Toomre, Derek; Bewersdorf, Joerg

2013-01-01

Newly developed scientific complementary metal–oxide–semiconductor (sCMOS) cameras have the potential to dramatically accelerate data acquisition in single-molecule switching nanoscopy (SMSN) while simultaneously increasing the effective quantum efficiency. However, sCMOS-intrinsic pixel-dependent readout noise substantially reduces the localization precision and introduces localization artifacts. Here we present algorithms that overcome these limitations and provide unbiased, precise localization of single molecules at the theoretical limit. In combination with a multi-emitter fitting algorithm, we demonstrate single-molecule localization super-resolution imaging at up to 32 reconstructed images/second (recorded at 1,600–3,200 camera frames/second) in both fixed and living cells. PMID:23708387

Gpufit: An open-source toolkit for GPU-accelerated curve fitting.

PubMed

Przybylski, Adrian; Thiel, Björn; Keller-Findeisen, Jan; Stock, Bernd; Bates, Mark

2017-11-16

We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets.

Compact full-motion video hyperspectral cameras: development, image processing, and applications

NASA Astrophysics Data System (ADS)

Kanaev, A. V.

2015-10-01

Emergence of spectral pixel-level color filters has enabled development of hyper-spectral Full Motion Video (FMV) sensors operating in visible (EO) and infrared (IR) wavelengths. The new class of hyper-spectral cameras opens broad possibilities of its utilization for military and industry purposes. Indeed, such cameras are able to classify materials as well as detect and track spectral signatures continuously in real time while simultaneously providing an operator the benefit of enhanced-discrimination-color video. Supporting these extensive capabilities requires significant computational processing of the collected spectral data. In general, two processing streams are envisioned for mosaic array cameras. The first is spectral computation that provides essential spectral content analysis e.g. detection or classification. The second is presentation of the video to an operator that can offer the best display of the content depending on the performed task e.g. providing spatial resolution enhancement or color coding of the spectral analysis. These processing streams can be executed in parallel or they can utilize each other's results. The spectral analysis algorithms have been developed extensively, however demosaicking of more than three equally-sampled spectral bands has been explored scarcely. We present unique approach to demosaicking based on multi-band super-resolution and show the trade-off between spatial resolution and spectral content. Using imagery collected with developed 9-band SWIR camera we demonstrate several of its concepts of operation including detection and tracking. We also compare the demosaicking results to the results of multi-frame super-resolution as well as to the combined multi-frame and multiband processing.

Adaptive-gain fast super-twisting sliding mode fault tolerant control for a reusable launch vehicle in reentry phase.

PubMed

Zhang, Yao; Tang, Shengjing; Guo, Jie

2017-11-01

In this paper, a novel adaptive-gain fast super-twisting (AGFST) sliding mode attitude control synthesis is carried out for a reusable launch vehicle subject to actuator faults and unknown disturbances. According to the fast nonsingular terminal sliding mode surface (FNTSMS) and adaptive-gain fast super-twisting algorithm, an adaptive fault tolerant control law for the attitude stabilization is derived to protect against the actuator faults and unknown uncertainties. Firstly, a second-order nonlinear control-oriented model for the RLV is established by feedback linearization method. And on the basis a fast nonsingular terminal sliding mode (FNTSM) manifold is designed, which provides fast finite-time global convergence and avoids singularity problem as well as chattering phenomenon. Based on the merits of the standard super-twisting (ST) algorithm and fast reaching law with adaption, a novel adaptive-gain fast super-twisting (AGFST) algorithm is proposed for the finite-time fault tolerant attitude control problem of the RLV without any knowledge of the bounds of uncertainties and actuator faults. The important feature of the AGFST algorithm includes non-overestimating the values of the control gains and faster convergence speed than the standard ST algorithm. A formal proof of the finite-time stability of the closed-loop system is derived using the Lyapunov function technique. An estimation of the convergence time and accurate expression of convergence region are also provided. Finally, simulations are presented to illustrate the effectiveness and superiority of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Controlled power delivery for super-resolution imaging of biological samples using digital micromirror device

NASA Astrophysics Data System (ADS)

Valiya Peedikakkal, Liyana; Cadby, Ashley

2017-02-01

Localization based super resolution images of a biological sample is generally achieved by using high power laser illumination with long exposure time which unfortunately increases photo-toxicity of a sample, making super resolution microscopy, in general, incompatible with live cell imaging. Furthermore, the limitation of photobleaching reduces the ability to acquire time lapse images of live biological cells using fluorescence microscopy. Digital Light Processing (DLP) technology can deliver light at grey scale levels by flickering digital micromirrors at around 290 Hz enabling highly controlled power delivery to samples. In this work, Digital Micromirror Device (DMD) is implemented in an inverse Schiefspiegler telescope setup to control the power and pattern of illumination for super resolution microscopy. We can achieve spatial and temporal patterning of illumination by controlling the DMD pixel by pixel. The DMD allows us to control the power and spatial extent of the laser illumination. We have used this to show that we can reduce the power delivered to the sample to allow for longer time imaging in one area while achieving sub-diffraction STORM imaging in another using higher power densities.

Multispectral image enhancement processing for microsat-borne imager

NASA Astrophysics Data System (ADS)

Sun, Jianying; Tan, Zheng; Lv, Qunbo; Pei, Linlin

2017-10-01

With the rapid development of remote sensing imaging technology, the micro satellite, one kind of tiny spacecraft, appears during the past few years. A good many studies contribute to dwarfing satellites for imaging purpose. Generally speaking, micro satellites weigh less than 100 kilograms, even less than 50 kilograms, which are slightly larger or smaller than the common miniature refrigerators. However, the optical system design is hard to be perfect due to the satellite room and weight limitation. In most cases, the unprocessed data captured by the imager on the microsatellite cannot meet the application need. Spatial resolution is the key problem. As for remote sensing applications, the higher spatial resolution of images we gain, the wider fields we can apply them. Consequently, how to utilize super resolution (SR) and image fusion to enhance the quality of imagery deserves studying. Our team, the Key Laboratory of Computational Optical Imaging Technology, Academy Opto-Electronics, is devoted to designing high-performance microsat-borne imagers and high-efficiency image processing algorithms. This paper addresses a multispectral image enhancement framework for space-borne imagery, jointing the pan-sharpening and super resolution techniques to deal with the spatial resolution shortcoming of microsatellites. We test the remote sensing images acquired by CX6-02 satellite and give the SR performance. The experiments illustrate the proposed approach provides high-quality images.

Beyond maximum entropy: Fractal Pixon-based image reconstruction

NASA Technical Reports Server (NTRS)

Puetter, Richard C.; Pina, R. K.

1994-01-01

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.

Time multiplexing super-resolution nanoscopy based on the Brownian motion of gold nanoparticles

NASA Astrophysics Data System (ADS)

Ilovitsh, Tali; Ilovitsh, Asaf; Wagner, Omer; Zalevsky, Zeev

2017-02-01

Super-resolution localization microscopy can overcome the diffraction limit and achieve a tens of order improvement in resolution. It requires labeling the sample with fluorescent probes followed with their repeated cycles of activation and photobleaching. This work presents an alternative approach that is free from direct labeling and does not require the activation and photobleaching cycles. Fluorescently labeled gold nanoparticles in a solution are distributed on top of the sample. The nanoparticles move in a random Brownian motion, and interact with the sample. By obscuring different areas in the sample, the nanoparticles encode the sub-wavelength features. A sequence of images of the sample is captured and decoded by digital post processing to create the super-resolution image. The achievable resolution is limited by the additive noise and the size of the nanoparticles. Regular nanoparticles with diameter smaller than 100nm are barely seen in a conventional bright field microscope, thus fluorescently labeled gold nanoparticles were used, with proper

Super-resolution with a positive epsilon multi-quantum-well super-lens

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bak, A. O.; Giannini, V.; Maier, S. A.

2013-12-23

We design an anisotropic and dichroic quantum metamaterial that is able to achieve super-resolution without the need for a negative permittivity. When exploring the parameters of the structure, we take into account the limits of semiconductor fabrication technology based on quantum well stacks. By heavily doping the structure with free electrons, we infer an anisotropic effective medium with a prolate ellipsoid dispersion curve which allows for near-diffractionless propagation of light (similar to an epsilon-near-zero hyperbolic lens). This, coupled with low absorption, allows us to resolve images at the sub-wavelength scale at distances 6 times greater than equivalent natural materials.

Automatic Near-Real-Time Image Processing Chain for Very High Resolution Optical Satellite Data

NASA Astrophysics Data System (ADS)

Ostir, K.; Cotar, K.; Marsetic, A.; Pehani, P.; Perse, M.; Zaksek, K.; Zaletelj, J.; Rodic, T.

2015-04-01

In response to the increasing need for automatic and fast satellite image processing SPACE-SI has developed and implemented a fully automatic image processing chain STORM that performs all processing steps from sensor-corrected optical images (level 1) to web-delivered map-ready images and products without operator's intervention. Initial development was tailored to high resolution RapidEye images, and all crucial and most challenging parts of the planned full processing chain were developed: module for automatic image orthorectification based on a physical sensor model and supported by the algorithm for automatic detection of ground control points (GCPs); atmospheric correction module, topographic corrections module that combines physical approach with Minnaert method and utilizing anisotropic illumination model; and modules for high level products generation. Various parts of the chain were implemented also for WorldView-2, THEOS, Pleiades, SPOT 6, Landsat 5-8, and PROBA-V. Support of full-frame sensor currently in development by SPACE-SI is in plan. The proposed paper focuses on the adaptation of the STORM processing chain to very high resolution multispectral images. The development concentrated on the sub-module for automatic detection of GCPs. The initially implemented two-step algorithm that worked only with rasterized vector roads and delivered GCPs with sub-pixel accuracy for the RapidEye images, was improved with the introduction of a third step: super-fine positioning of each GCP based on a reference raster chip. The added step exploits the high spatial resolution of the reference raster to improve the final matching results and to achieve pixel accuracy also on very high resolution optical satellite data.

Super-resolution from single photon emission: toward biological application

NASA Astrophysics Data System (ADS)

Moreva, E.; Traina, P.; Forneris, J.; Ditalia Tchernij, S.; Guarina, L.; Franchino, C.; Picollo, F.; Ruo Berchera, I.; Brida, G.; Degiovanni, I. P.; Carabelli, V.; Olivero, P.; Genovese, M.

2017-08-01

Properties of quantum light represent a tool for overcoming limits of classical optics. Several experiments have demonstrated this advantage ranging from quantum enhanced imaging to quantum illumination. In this work, experimental demonstration of quantum-enhanced resolution in confocal fluorescence microscopy will be presented. This is achieved by exploiting the non-classical photon statistics of fluorescence emission of single nitrogen-vacancy (NV) color centers in diamond. By developing a general model of super-resolution based on the direct sampling of the kth-order autocorrelation function of the photoluminescence signal, we show the possibility to resolve, in principle, arbitrarily close emitting centers. Finally, possible applications of NV-based fluorescent nanodiamonds in biosensing and future developments will be presented.

In vivo super-resolution imaging of transient retinal phototropism evoked by oblique light stimulation.

PubMed

Lu, Yiming; Liu, Changgeng; Yao, Xincheng

2018-05-01

Rod-dominated transient retinal phototropism (TRP) has been observed in freshly isolated retinas, promising a noninvasive biomarker for objective assessment of retinal physiology. However, in vivo mapping of TRP is challenging due to its subcellular signal magnitude and fast time course. We report here a virtually structured detection-based super-resolution ophthalmoscope to achieve subcellular spatial resolution and millisecond temporal resolution for in vivo imaging of TRP. Spatiotemporal properties of in vivo TRP were characterized corresponding to variable light intensity stimuli, confirming that TRP is tightly correlated with early stages of phototransduction. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Super-resolution and super-localization microscopy: A novel tool for imaging chemical and biological processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Bin

2015-01-01

Optical microscopy imaging of single molecules and single particles is an essential method for studying fundamental biological and chemical processes at the molecular and nanometer scale. The best spatial resolution (~ λ/2) achievable in traditional optical microscopy is governed by the diffraction of light. However, single molecule-based super-localization and super-resolution microscopy imaging techniques have emerged in the past decade. Individual molecules can be localized with nanometer scale accuracy and precision for studying of biological and chemical processes.This work uncovered the heterogeneous properties of the pore structures. In this dissertation, the coupling of molecular transport and catalytic reaction at the singlemore » molecule and single particle level in multilayer mesoporous nanocatalysts was elucidated. Most previous studies dealt with these two important phenomena separately. A fluorogenic oxidation reaction of non-fluorescent amplex red to highly fluorescent resorufin was tested. The diffusion behavior of single resorufin molecules in aligned nanopores was studied using total internal reflection fluorescence microscopy (TIRFM).« less

CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography)

PubMed Central

Siegel, Nisan; Storrie, Brian; Bruce, Marc

2016-01-01

FINCH holographic fluorescence microscopy creates high resolution super-resolved images with enhanced depth of focus. The simple addition of a real-time Nipkow disk confocal image scanner in a conjugate plane of this incoherent holographic system is shown to reduce the depth of focus, and the combination of both techniques provides a simple way to enhance the axial resolution of FINCH in a combined method called “CINCH”. An important feature of the combined system allows for the simultaneous real-time image capture of widefield and holographic images or confocal and confocal holographic images for ready comparison of each method on the exact same field of view. Additional GPU based complex deconvolution processing of the images further enhances resolution. PMID:26839443

Emergence of an optimal search strategy from a simple random walk

PubMed Central

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-01-01

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths. PMID:23804445

Emergence of an optimal search strategy from a simple random walk.

PubMed

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-09-06

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.

Super-resolution chemical imaging with dynamic placement of plasmonic hotspots

NASA Astrophysics Data System (ADS)

Olson, Aeli P.; Ertsgaard, Christopher T.; McKoskey, Rachel M.; Rich, Isabel S.; Lindquist, Nathan C.

2015-08-01

We demonstrate dynamic placement of plasmonic "hotspots" for super-resolution chemical imaging via Surface Enhanced Raman Spectroscopy (SERS). A silver nanohole array surface was coated with biological samples and illuminated with a laser. Due to the large plasmonic field enhancements, blinking behavior of the SERS hotspots was observed and processed using a Stochastic Optical Reconstruction Microscopy (STORM) algorithm enabling localization to within 10 nm. However, illumination of the sample with a single static laser beam (i.e., a slightly defocused Gaussian beam) only produced SERS hotspots in fixed locations on the surface, leaving noticeable gaps in any final image. But, by using a spatial light modulator (SLM), the illumination profile of the beam could be altered, shifting any hotspots across the nanohole array surface in sub-wavelength steps. Therefore, by properly structuring an illuminating light field with the SLM, we show the possibility of positioning plasmonic hotspots over a metallic nanohole surface on-the-fly. Using this and our SERS-STORM imaging technique, we show potential for high-resolution chemical imaging without the noticeable gaps that were present with static laser illumination. Interestingly, even illuminating the surface with randomly shifting SLM phase profiles was sufficient to completely fill in a wide field of view for super-resolution SERS imaging of a single strand of 100-nm thick collagen protein fibrils. Images were then compared to those obtained with a scanning electron microscope (SEM). Additionally, we explored alternative methods of phase shifting other than holographic illumination through the SLM to create localization of hotspots necessary for SERS-STORM imaging.

Fast, label-free super-resolution live-cell imaging using rotating coherent scattering (ROCS) microscopy

NASA Astrophysics Data System (ADS)

Jünger, Felix; Olshausen, Philipp V.; Rohrbach, Alexander

2016-07-01

Living cells are highly dynamic systems with cellular structures being often below the optical resolution limit. Super-resolution microscopes, usually based on fluorescence cell labelling, are usually too slow to resolve small, dynamic structures. We present a label-free microscopy technique, which can generate thousands of super-resolved, high contrast images at a frame rate of 100 Hertz and without any post-processing. The technique is based on oblique sample illumination with coherent light, an approach believed to be not applicable in life sciences because of too many interference artefacts. However, by circulating an incident laser beam by 360° during one image acquisition, relevant image information is amplified. By combining total internal reflection illumination with dark-field detection, structures as small as 150 nm become separable through local destructive interferences. The technique images local changes in refractive index through scattered laser light and is applied to living mouse macrophages and helical bacteria revealing unexpected dynamic processes.

Fast, label-free super-resolution live-cell imaging using rotating coherent scattering (ROCS) microscopy

PubMed Central

Jünger, Felix; Olshausen, Philipp v.; Rohrbach, Alexander

2016-01-01

Living cells are highly dynamic systems with cellular structures being often below the optical resolution limit. Super-resolution microscopes, usually based on fluorescence cell labelling, are usually too slow to resolve small, dynamic structures. We present a label-free microscopy technique, which can generate thousands of super-resolved, high contrast images at a frame rate of 100 Hertz and without any post-processing. The technique is based on oblique sample illumination with coherent light, an approach believed to be not applicable in life sciences because of too many interference artefacts. However, by circulating an incident laser beam by 360° during one image acquisition, relevant image information is amplified. By combining total internal reflection illumination with dark-field detection, structures as small as 150 nm become separable through local destructive interferences. The technique images local changes in refractive index through scattered laser light and is applied to living mouse macrophages and helical bacteria revealing unexpected dynamic processes. PMID:27465033

New developments in super-resolution for GaoFen-4

NASA Astrophysics Data System (ADS)

Li, Feng; Fu, Jie; Xin, Lei; Liu, Yuhong; Liu, Zhijia

2017-10-01

In this paper, the application of super resolution (SR, restoring a high spatial resolution image from a series of low resolution images of the same scene) techniques to GaoFen(GF)-4, which is the most advanced geostationaryorbit earth observing satellite in China, remote sensing images is investigated and tested. SR has been a hot research area for decades, but one of the barriers of applying SR in remote sensing community is the time slot between those low resolution (LR) images acquisition. In general, the longer the time slot, the less reliable the reconstruction. GF-4 has the unique advantage of capturing a sequence of LR of the same region in minutes, i.e. working as a staring camera from the point view of SR. This is the first experiment of applying super resolution to a sequence of low resolution images captured by GF-4 within a short time period. In this paper, we use Maximum a Posteriori (MAP) to solve the ill-conditioned problem of SR. Both the wavelet transform and the curvelet transform are used to setup a sparse prior for remote sensing images. By combining several images of both the BeiJing and DunHuang regions captured by GF-4 our method can improve spatial resolution both visually and numerically. Experimental tests show that lots of detail cannot be observed in the captured LR images, but can be seen in the super resolved high resolution (HR) images. To help the evaluation, Google Earth image can also be referenced. Moreover, our experimental tests also show that the higher the temporal resolution, the better the HR images can be resolved. The study illustrates that the application for SR to geostationary-orbit based earth observation data is very feasible and worthwhile, and it holds the potential application for all other geostationary-orbit based earth observing systems.

Development of bimolecular fluorescence complementation using rsEGFP2 for detection and super-resolution imaging of protein-protein interactions in live cells

PubMed Central

Wang, Sheng; Ding, Miao; Chen, Xuanze; Chang, Lei; Sun, Yujie

2017-01-01

Direct visualization of protein-protein interactions (PPIs) at high spatial and temporal resolution in live cells is crucial for understanding the intricate and dynamic behaviors of signaling protein complexes. Recently, bimolecular fluorescence complementation (BiFC) assays have been combined with super-resolution imaging techniques including PALM and SOFI to visualize PPIs at the nanometer spatial resolution. RESOLFT nanoscopy has been proven as a powerful live-cell super-resolution imaging technique. With regard to the detection and visualization of PPIs in live cells with high temporal and spatial resolution, here we developed a BiFC assay using split rsEGFP2, a highly photostable and reversibly photoswitchable fluorescent protein previously developed for RESOLFT nanoscopy. Combined with parallelized RESOLFT microscopy, we demonstrated the high spatiotemporal resolving capability of a rsEGFP2-based BiFC assay by detecting and visualizing specifically the heterodimerization interactions between Bcl-xL and Bak as well as the dynamics of the complex on mitochondria membrane in live cells. PMID:28663931

Functional Analysis of Internal Moving Organs Using Super-Resolution Echography

NASA Astrophysics Data System (ADS)

Masuda, Kohji; Ishihara, Ken; Nagakura, Toshiaki; Tsuda, Takao; Furukawa, Toshiyuki; Maeda, Hajime; Kumagai, Sadatoshi; Kodama, Shinzo

1994-05-01

We have developed super-resolution echography to visualize instantaneous velocity and acceleration of internal organs from time-series echograms recorded by a high-frame-rate echograph. The algorithm for this method involves subtraction of two echograms, dividing the difference by the brightness gradient of the first echogram, and normalization of that result by the time interval between the two echograms. Velocity or acceleration is classified into some suitable colors and superimposed on the original B-mode image. Functional diagnosis of moving organs can be made by visualizing instantaneous velocity. In the case of the heart, hypokinesis can be distinguished from a normal heart by the value and the variation of colored parts representing instantaneous velocity. This can also be applied to the liver to observe pulsatile motion. By visualizing instantaneous acceleration, increase or decrease of velocity can be detected. Throb timing and the location of arrhythmia in a heart can be observed. This method has the possibility of contributing to noninvasive functional and characteristic evaluation.

Developmental approach towards high resolution optical coherence tomography for glaucoma diagnostics

NASA Astrophysics Data System (ADS)

Kemper, Björn; Ketelhut, Steffi; Heiduschka, Peter; Thorn, Marie; Larsen, Michael; Schnekenburger, Jürgen

2018-02-01

Glaucoma is caused by a pathological rise in the intraocular pressure, which results in a progressive loss of vision by a damage to retinal cells and the optical nerve head. Early detection of pressure-induced damage is thus essential for the reduction of eye pressure and to prevent severe incapacity or blindness. Within the new European Project GALAHAD (Glaucoma Advanced, Label free High Resolution Automated OCT Diagnostics), we will develop a new low-cost and high-resolution OCT system for the early detection of glaucoma. The device is designed to improve diagnosis based on a new system of optical coherence tomography. Although OCT systems are at present available in ophthalmology centres, high-resolution devices are extremely expensive. The novelty of the new Galahad system is its super wideband light source to achieve high image resolution at a reasonable cost. Proof of concept experiments with cell and tissue Glaucoma test standards and animal models are planned for the test of the new optical components and new algorithms performance for the identification of Glaucoma associated cell and tissue structures. The intense training of the software systems with various samples should result in a increased sensitivity and specificity of the OCT software system.

Estimation of Cardiopulmonary Parameters From Ultra Wideband Radar Measurements Using the State Space Method.

PubMed

Naishadham, Krishna; Piou, Jean E; Ren, Lingyun; Fathy, Aly E

2016-12-01

Ultra wideband (UWB) Doppler radar has many biomedical applications, including remote diagnosis of cardiovascular disease, triage and real-time personnel tracking in rescue missions. It uses narrow pulses to probe the human body and detect tiny cardiopulmonary movements by spectral analysis of the backscattered electromagnetic (EM) field. With the help of super-resolution spectral algorithms, UWB radar is capable of increased accuracy for estimating vital signs such as heart and respiration rates in adverse signal-to-noise conditions. A major challenge for biomedical radar systems is detecting the heartbeat of a subject with high accuracy, because of minute thorax motion (less than 0.5 mm) caused by the heartbeat. The problem becomes compounded by EM clutter and noise in the environment. In this paper, we introduce a new algorithm based on the state space method (SSM) for the extraction of cardiac and respiration rates from UWB radar measurements. SSM produces range-dependent system poles that can be classified parametrically with spectral peaks at the cardiac and respiratory frequencies. It is shown that SSM produces accurate estimates of the vital signs without producing harmonics and inter-modulation products that plague signal resolution in widely used FFT spectrograms.

Super-resolution photoacoustic microscopy using a localized near-field of a plasmonic nanoaperture: a three-dimensional simulation study

NASA Astrophysics Data System (ADS)

Park, Byullee; Lee, Hongki; Upputuri, Paul Kumar; Pramanik, Manojit; Kim, Donghyun; Kim, Chulhong

2018-02-01

Super-resolution microscopy has been increasingly important to delineate nanoscale biological structures or nanoparticles. With these increasing demands, several imaging modalities, including super-resolution fluorescence microscope (SRFM) and electron microscope (EM), have been developed and commercialized. These modalities achieve nanoscale resolution, however, SRFM cannot image without fluorescence, and sample preparation of EM is not suitable for biological specimens. To overcome those disadvantages, we have numerically studied the possibility of superresolution photoacoustic microscopy (SR-PAM) based on near-field localization of light. Photoacoustic (PA) signal is generally acquired based on optical absorption contrast; thus it requires no agents or pre-processing for the samples. The lateral resolution of the conventional photoacoustic microscopy is limited to 200 nm by diffraction limit, therefore reducing the lateral resolution is a major research impetus. Our approach to breaking resolution limit is to use laser pulses of extremely small spot size as a light source. In this research, we simulated the PA signal by constructing the three dimensional SR-PAM system environment using the k-Wave toolbox. As the light source, we simulated ultrashort light pulses using geometrical nanoaperture with near-field localization of surface plasmons. Through the PA simulation, we have successfully distinguish cuboids spaced 3 nm apart. In the near future, we will develop the SR-PAM and it will contribute to biomedical and material sciences.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

PubMed

Shrestha, Akritee; Bergquist, Savannah; Montz, Ellen; Rose, Sherri

2017-12-15

To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. 2012-2013 Truven MarketScan database. We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC + CCS diagnostic variables. Performance was evaluated based on cross-validated R 2 and predictive ratios. Results show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas. Literature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs. © Health Research and Educational Trust.

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

A statistical look at the retrieval of exoplanetary atmospheres of super Earths and giant planets

NASA Astrophysics Data System (ADS)

Rocchetto, Marco; Waldmann, Ingo Peter; Tinetti, Giovanna; Yurchenko, Sergey; Tennyson, Jonathan

2015-08-01

Over the past decades transit spectroscopy has become one of the pioneering methods to characterise exoplanetary atmospheres. With the increasing number of observations, and the advent of new ground and spaced based instruments, it is now crucial to find the most optimal and objective methodologies to interpret these data, and understand the information content they convey. This is particularly true for smaller and fainter super Earth type planets.In this conference we will present a new take on the spectral retrieval of transiting planets, with particular focus on super Earth atmospheres. TauREx (Waldmann et al. 2015a,b.) is a new line-by-line radiative transfer atmospheric retrieval framework for transmission and emission spectroscopy of exoplanetary atmospheres, optimised for hot Jupiters and super Earths. The code has been built from scratch with the ideas of scalability, flexibility and automation. This allows to run retrievals with minimum user input that can be scaled to large cluster computing. Priors on the number and types of molecules considered are automatically determined using a custom built pattern recognition algorithm able to identify the most likely absorbers/emitters in the exoplanetary spectra, minimising the human bias in selecting the major atmospheric constituents.Using these tools, we investigate the impact of signal to noise, spectral resolution and wavelength coverage on the retrievability of individual model parameters from transit spectra of super Earths, and put our models to test (Rocchetto et al. 2015). Characterisation of the atmospheres of super Earths through transit spectroscopy is paramount, as it can provide an indirect - and so far unique - way to probe the nature of these planets. For the first time we analyse in a systematic way large grids of spectra generated for different observing scenarios. We perform thousands of retrievals aimed to fully map the degeneracies and understand the statistics of current exoplanetary retrieval models, in the limiting signal-to-noise regime of super Earth observations.

Time multiplexing based extended depth of focus imaging.

PubMed

Ilovitsh, Asaf; Zalevsky, Zeev

2016-01-01

We propose to utilize the time multiplexing super resolution method to extend the depth of focus of an imaging system. In standard time multiplexing, the super resolution is achieved by generating duplication of the optical transfer function in the spectrum domain, by the use of moving gratings. While this improves the spatial resolution, it does not increase the depth of focus. By changing the gratings frequency and, by that changing the duplication positions, it is possible to obtain an extended depth of focus. The proposed method is presented analytically, demonstrated via numerical simulations and validated by a laboratory experiment.

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

NASA Astrophysics Data System (ADS)

Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

2016-03-01

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.

PubMed

Umehara, Kensuke; Ota, Junko; Ishida, Takayuki

2017-10-18

In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.

Microsphere-aided optical microscopy and its applications for super-resolution imaging

NASA Astrophysics Data System (ADS)

Upputuri, Paul Kumar; Pramanik, Manojit

2017-12-01

The spatial resolution of a standard optical microscope (SOM) is limited by diffraction. In visible spectrum, SOM can provide ∼ 200 nm resolution. To break the diffraction limit several approaches were developed including scanning near field microscopy, metamaterial super-lenses, nanoscale solid immersion lenses, super-oscillatory lenses, confocal fluorescence microscopy, techniques that exploit non-linear response of fluorophores like stimulated emission depletion microscopy, stochastic optical reconstruction microscopy, etc. Recently, photonic nanojet generated by a dielectric microsphere was used to break the diffraction limit. The microsphere-approach is simple, cost-effective and can be implemented under a standard microscope, hence it has gained enormous attention for super-resolution imaging. In this article, we briefly review the microsphere approach and its applications for super-resolution imaging in various optical imaging modalities.

PDF file encryption on mobile phone using super-encryption of Variably Modified Permutation Composition (VMPC) and two square cipher algorithm

NASA Astrophysics Data System (ADS)

Rachmawati, D.; Budiman, M. A.; Atika, F.

2018-03-01

Data security is becoming one of the most significant challenges in the digital world. Retrieval of data by unauthorized parties will result in harm to the owner of the data. PDF data are also susceptible to data security disorder. These things affect the security of the information. To solve the security problem, it needs a method to maintain the protection of the data, such as cryptography. In cryptography, several algorithms can encode data, one of them is Two Square Cipher algorithm which is a symmetric algorithm. At this research, Two Square Cipher algorithm has already developed into a 16 x 16 key aims to enter the various plaintexts. However, for more enhancement security it will be combined with the VMPC algorithm which is a symmetric algorithm. The combination of the two algorithms is called with the super-encryption. At this point, the data already can be stored on a mobile phone allowing users to secure data flexibly and can be accessed anywhere. The application of PDF document security on this research built by Android-platform. At this study will also calculate the complexity of algorithms and process time. Based on the test results the complexity of the algorithm is θ (n) for Two Square Cipher and θ (n) for VMPC algorithm, so the complexity of the super-encryption is also θ (n). VMPC algorithm processing time results quicker than on Two Square Cipher. And the processing time is directly proportional to the length of the plaintext and passwords.

Enhancing Analytical Separations Using Super-Resolution Microscopy

NASA Astrophysics Data System (ADS)

Moringo, Nicholas A.; Shen, Hao; Bishop, Logan D. C.; Wang, Wenxiao; Landes, Christy F.

2018-04-01

Super-resolution microscopy is becoming an invaluable tool to investigate structure and dynamics driving protein interactions at interfaces. In this review, we highlight the applications of super-resolution microscopy for quantifying the physics and chemistry that occur between target proteins and stationary-phase supports during chromatographic separations. Our discussion concentrates on the newfound ability of super-resolved single-protein spectroscopy to inform theoretical parameters via quantification of adsorption-desorption dynamics, protein unfolding, and nanoconfined transport.

Reducible dictionaries for single image super-resolution based on patch matching and mean shifting

NASA Astrophysics Data System (ADS)

Rasti, Pejman; Nasrollahi, Kamal; Orlova, Olga; Tamberg, Gert; Moeslund, Thomas B.; Anbarjafari, Gholamreza

2017-03-01

A single-image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) images and their corresponding low resolution (LR) representations. First, HR images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion.

PubMed

Zhao, Wenyi; Zhang, Chao

2008-07-01

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.

Nanoscopy for nanoscience: how super-resolution microscopy extends imaging for nanotechnology.

PubMed

Johnson, Sam A

2015-01-01

Imaging methods have presented scientists with powerful means of investigation for centuries. The ability to resolve structures using light microscopes is though limited to around 200 nm. Fluorescence-based super-resolution light microscopy techniques of several principles and methods have emerged in recent years and offer great potential to extend the capabilities of microscopy. This resolution improvement is especially promising for nanoscience where the imaging of nanoscale structures is inherently restricted by the resolution limit of standard forms of light microscopy. Resolution can be improved by several distinct approaches including structured illumination microscopy, stimulated emission depletion, and single-molecule positioning methods such as photoactivated localization microscopy and stochastic optical reconstruction microscopy and several derivative variations of each of these. These methods involve substantial differences in the resolutions achievable in the different axes, speed of acquisition, compatibility with different labels, ease of use, hardware complexity, and compatibility with live biological samples. The field of super-resolution imaging and its application to nanotechnology is relatively new and still rapidly developing. An overview of how these methods may be used with nanomaterials is presented with some examples of pioneering uses of these approaches. © 2014 Wiley Periodicals, Inc.

Single image super-resolution based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

2018-03-01

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

Super-Resolution Enhancement From Multiple Overlapping Images: A Fractional Area Technique

NASA Astrophysics Data System (ADS)

Michaels, Joshua A.

With the availability of large quantities of relatively low-resolution data from several decades of space borne imaging, methods of creating an accurate, higher-resolution image from the multiple lower-resolution images (i.e. super-resolution), have been developed almost since such imagery has been around. The fractional-area super-resolution technique developed in this thesis has never before been documented. Satellite orbits, like Landsat, have a quantifiable variation, which means each image is not centered on the exact same spot more than once and the overlapping information from these multiple images may be used for super-resolution enhancement. By splitting a single initial pixel into many smaller, desired pixels, a relationship can be created between them using the ratio of the area within the initial pixel. The ideal goal for this technique is to obtain smaller pixels with exact values and no error, yielding a better potential result than those methods that yield interpolated pixel values with consequential loss of spatial resolution. A Fortran 95 program was developed to perform all calculations associated with the fractional-area super-resolution technique. The fractional areas are calculated using traditional trigonometry and coordinate geometry and Linear Algebra Package (LAPACK; Anderson et al., 1999) is used to solve for the higher-resolution pixel values. In order to demonstrate proof-of-concept, a synthetic dataset was created using the intrinsic Fortran random number generator and Adobe Illustrator CS4 (for geometry). To test the real-life application, digital pictures from a Sony DSC-S600 digital point-and-shoot camera with a tripod were taken of a large US geological map under fluorescent lighting. While the fractional-area super-resolution technique works in perfect synthetic conditions, it did not successfully produce a reasonable or consistent solution in the digital photograph enhancement test. The prohibitive amount of processing time (up to 60 days for a relatively small enhancement area) severely limits the practical usefulness of fraction-area super-resolution. Fractional-area super-resolution is very sensitive to relative input image co-registration, which must be accurate to a sub-pixel degree. However, use of this technique, if input conditions permit, could be applied as a "pinpoint" super-resolution technique. Such an application could be possible by only applying it to only very small areas with very good input image co-registration.

Signal Characteristics of Super-Resolution Near-Field Structure Disks with 100 GB Capacity

NASA Astrophysics Data System (ADS)

Kim, Jooho; Hwang, Inoh; Kim, Hyunki; Park, Insik; Tominaga, Junji

2005-05-01

We report the basic characteristics of super resolution near-field structure (Super-RENS) media at a blue laser optical system (laser wavelength 405 nm, numerical aperture 0.85). Using a novel write once read many (WORM) structure for a blue laser system, we obtained a carrier-to-noise ratio (CNR) above 33 dB from the signal of the 37.5 nm mark length, which is equivalent to a 100 GB capacity with a 0.32 micrometer track pitch, and an eye pattern for 50 GB (2T: 75 nm) capacity using a patterned signal. Using a novel super-resolution material (tellurium, Te) with low super-resolution readout power, we also improved the read stability.

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

PubMed Central

Huang, Xiwei; Jiang, Yu; Liu, Xu; Xu, Hang; Han, Zhi; Rong, Hailong; Yang, Haiping; Yan, Mei; Yu, Hao

2016-01-01

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications. PMID:27827837

Resolution enhancement in deep-tissue nanoparticle imaging based on plasmonic saturated excitation microscopy

NASA Astrophysics Data System (ADS)

Deka, Gitanjal; Nishida, Kentaro; Mochizuki, Kentaro; Ding, Hou-Xian; Fujita, Katsumasa; Chu, Shi-Wei

2018-03-01

Recently, many resolution enhancing techniques are demonstrated, but most of them are severely limited for deep tissue applications. For example, wide-field based localization techniques lack the ability of optical sectioning, and structured light based techniques are susceptible to beam distortion due to scattering/aberration. Saturated excitation (SAX) microscopy, which relies on temporal modulation that is less affected when penetrating into tissues, should be the best candidate for deep-tissue resolution enhancement. Nevertheless, although fluorescence saturation has been successfully adopted in SAX, it is limited by photobleaching, and its practical resolution enhancement is less than two-fold. Recently, we demonstrated plasmonic SAX which provides bleaching-free imaging with three-fold resolution enhancement. Here we show that the three-fold resolution enhancement is sustained throughout the whole working distance of an objective, i.e., 200 μm, which is the deepest super-resolution record to our knowledge, and is expected to extend into deeper tissues. In addition, SAX offers the advantage of background-free imaging by rejecting unwanted scattering background from biological tissues. This study provides an inspirational direction toward deep-tissue super-resolution imaging and has the potential in tumor monitoring and beyond.

Dances with Membranes: Breakthroughs from Super-resolution Imaging

PubMed Central

Curthoys, Nikki M.; Parent, Matthew; Mlodzianoski, Michael; Nelson, Andrew J.; Lilieholm, Jennifer; Butler, Michael B.; Valles, Matthew; Hess, Samuel T.

2017-01-01

Biological membrane organization mediates numerous cellular functions and has also been connected with an immense number of human diseases. However, until recently, experimental methodologies have been unable to directly visualize the nanoscale details of biological membranes, particularly in intact living cells. Numerous models explaining membrane organization have been proposed, but testing those models has required indirect methods; the desire to directly image proteins and lipids in living cell membranes is a strong motivation for the advancement of technology. The development of super-resolution microscopy has provided powerful tools for quantification of membrane organization at the level of individual proteins and lipids, and many of these tools are compatible with living cells. Previously inaccessible questions are now being addressed, and the field of membrane biology is developing rapidly. This chapter discusses how the development of super-resolution microscopy has led to fundamental advances in the field of biological membrane organization. We summarize the history and some models explaining how proteins are organized in cell membranes, and give an overview of various super-resolution techniques and methods of quantifying super-resolution data. We discuss the application of super-resolution techniques to membrane biology in general, and also with specific reference to the fields of actin and actin-binding proteins, virus infection, mitochondria, immune cell biology, and phosphoinositide signaling. Finally, we present our hopes and expectations for the future of super-resolution microscopy in the field of membrane biology. PMID:26015281

Super-resolved all-refocused image with a plenoptic camera

NASA Astrophysics Data System (ADS)

Wang, Xiang; Li, Lin; Hou, Guangqi

2015-12-01

This paper proposes an approach to produce the super-resolution all-refocused images with the plenoptic camera. The plenoptic camera can be produced by putting a micro-lens array between the lens and the sensor in a conventional camera. This kind of camera captures both the angular and spatial information of the scene in one single shot. A sequence of digital refocused images, which are refocused at different depth, can be produced after processing the 4D light field captured by the plenoptic camera. The number of the pixels in the refocused image is the same as that of the micro-lens in the micro-lens array. Limited number of the micro-lens will result in poor low resolution refocused images. Therefore, not enough details will exist in these images. Such lost details, which are often high frequency information, are important for the in-focus part in the refocused image. We decide to super-resolve these in-focus parts. The result of image segmentation method based on random walks, which works on the depth map produced from the 4D light field data, is used to separate the foreground and background in the refocused image. And focusing evaluation function is employed to determine which refocused image owns the clearest foreground part and which one owns the clearest background part. Subsequently, we employ single image super-resolution method based on sparse signal representation to process the focusing parts in these selected refocused images. Eventually, we can obtain the super-resolved all-focus image through merging the focusing background part and the focusing foreground part in the way of digital signal processing. And more spatial details will be kept in these output images. Our method will enhance the resolution of the refocused image, and just the refocused images owning the clearest foreground and background need to be super-resolved.

Oblique reconstructions in tomosynthesis. II. Super-resolution

PubMed Central

Acciavatti, Raymond J.; Maidment, Andrew D. A.

2013-01-01

Purpose: In tomosynthesis, super-resolution has been demonstrated using reconstruction planes parallel to the detector. Super-resolution allows for subpixel resolution relative to the detector. The purpose of this work is to develop an analytical model that generalizes super-resolution to oblique reconstruction planes. Methods: In a digital tomosynthesis system, a sinusoidal test object is modeled along oblique angles (i.e., “pitches”) relative to the plane of the detector in a 3D divergent-beam acquisition geometry. To investigate the potential for super-resolution, the input frequency is specified to be greater than the alias frequency of the detector. Reconstructions are evaluated in an oblique plane along the extent of the object using simple backprojection (SBP) and filtered backprojection (FBP). By comparing the amplitude of the reconstruction against the attenuation coefficient of the object at various frequencies, the modulation transfer function (MTF) is calculated to determine whether modulation is within detectable limits for super-resolution. For experimental validation of super-resolution, a goniometry stand was used to orient a bar pattern phantom along various pitches relative to the breast support in a commercial digital breast tomosynthesis system. Results: Using theoretical modeling, it is shown that a single projection image cannot resolve a sine input whose frequency exceeds the detector alias frequency. The high frequency input is correctly visualized in SBP or FBP reconstruction using a slice along the pitch of the object. The Fourier transform of this reconstructed slice is maximized at the input frequency as proof that the object is resolved. Consistent with the theoretical results, experimental images of a bar pattern phantom showed super-resolution in oblique reconstructions. At various pitches, the highest frequency with detectable modulation was determined by visual inspection of the bar patterns. The dependency of the highest detectable frequency on pitch followed the same trend as the analytical model. It was demonstrated that super-resolution is not achievable if the pitch of the object approaches 90°, corresponding to the case in which the test frequency is perpendicular to the breast support. Only low frequency objects are detectable at pitches close to 90°. Conclusions: This work provides a platform for investigating super-resolution in oblique reconstructions for tomosynthesis. In breast imaging, this study should have applications in visualizing microcalcifications and other subtle signs of cancer. PMID:24320445

Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.

PubMed

Perez, Victor; Chang, Bo-Jui; Stelzer, Ernst Hans Karl

2016-11-16

Structured illumination microscopy relies on reconstruction algorithms to yield super-resolution images. Artifacts can arise in the reconstruction and affect the image quality. Current reconstruction methods involve a parametrized apodization function and a Wiener filter. Empirically tuning the parameters in these functions can minimize artifacts, but such an approach is subjective and produces volatile results. We present a robust and objective method that yields optimal results by two straightforward filtering steps with Richardson-Lucy-based deconvolutions. We provide a resource to identify artifacts in 2D-SIM images by analyzing two main reasons for artifacts, out-of-focus background and a fluctuating reconstruction spectrum. We show how the filtering steps improve images of test specimens, microtubules, yeast and mammalian cells.

Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution

NASA Astrophysics Data System (ADS)

Perez, Victor; Chang, Bo-Jui; Stelzer, Ernst Hans Karl

2016-11-01

Structured illumination microscopy relies on reconstruction algorithms to yield super-resolution images. Artifacts can arise in the reconstruction and affect the image quality. Current reconstruction methods involve a parametrized apodization function and a Wiener filter. Empirically tuning the parameters in these functions can minimize artifacts, but such an approach is subjective and produces volatile results. We present a robust and objective method that yields optimal results by two straightforward filtering steps with Richardson-Lucy-based deconvolutions. We provide a resource to identify artifacts in 2D-SIM images by analyzing two main reasons for artifacts, out-of-focus background and a fluctuating reconstruction spectrum. We show how the filtering steps improve images of test specimens, microtubules, yeast and mammalian cells.

Fast high resolution reconstruction in multi-slice and multi-view cMRI

NASA Astrophysics Data System (ADS)

Velasco Toledo, Nelson; Romero Castro, Eduardo

2015-01-01

Cardiac magnetic resonance imaging (cMRI) is an useful tool in diagnosis, prognosis and research since it functionally tracks the heart structure. Although useful, this imaging technique is limited in spatial resolution because heart is a constant moving organ, also there are other non controled conditions such as patient movements and volumetric changes during apnea periods when data is acquired, those conditions limit the time to capture high quality information. This paper presents a very fast and simple strategy to reconstruct high resolution 3D images from a set of low resolution series of 2D images. The strategy is based on an information reallocation algorithm which uses the DICOM header to relocate voxel intensities in a regular grid. An interpolation method is applied to fill empty places with estimated data, the interpolation resamples the low resolution information to estimate the missing information. As a final step a gaussian filter that denoises the final result. A reconstructed image evaluation is performed using as a reference a super-resolution reconstructed image. The evaluation reveals that the method maintains the general heart structure with a small loss in detailed information (edge sharpening and blurring), some artifacts related with input information quality are detected. The proposed method requires low time and computational resources.

swot: Super W Of Theta

NASA Astrophysics Data System (ADS)

Coupon, Jean; Leauthaud, Alexie; Kilbinger, Martin; Medezinski, Elinor

2017-07-01

SWOT (Super W Of Theta) computes two-point statistics for very large data sets, based on “divide and conquer” algorithms, mainly, but not limited to data storage in binary trees, approximation at large scale, parellelization (open MPI), and bootstrap and jackknife resampling methods “on the fly”. It currently supports projected and 3D galaxy auto and cross correlations, galaxy-galaxy lensing, and weighted histograms.

A positional misalignment correction method for Fourier ptychographic microscopy based on simulated annealing

NASA Astrophysics Data System (ADS)

Sun, Jiasong; Zhang, Yuzhen; Chen, Qian; Zuo, Chao

2017-02-01

Fourier ptychographic microscopy (FPM) is a newly developed super-resolution technique, which employs angularly varying illuminations and a phase retrieval algorithm to surpass the diffraction limit of a low numerical aperture (NA) objective lens. In current FPM imaging platforms, accurate knowledge of LED matrix's position is critical to achieve good recovery quality. Furthermore, considering such a wide field-of-view (FOV) in FPM, different regions in the FOV have different sensitivity of LED positional misalignment. In this work, we introduce an iterative method to correct position errors based on the simulated annealing (SA) algorithm. To improve the efficiency of this correcting process, large number of iterations for several images with low illumination NAs are firstly implemented to estimate the initial values of the global positional misalignment model through non-linear regression. Simulation and experimental results are presented to evaluate the performance of the proposed method and it is demonstrated that this method can both improve the quality of the recovered object image and relax the LED elements' position accuracy requirement while aligning the FPM imaging platforms.

Detecting breast microcalcifications using super-resolution ultrasound imaging: a clinical study

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Labyed, Yassin; Hanson, Kenneth; Sandoval, Daniel; Pohl, Jennifer; Williamson, Michael

2013-03-01

Imaging breast microcalcifications is crucial for early detection and diagnosis of breast cancer. It is challenging for current clinical ultrasound to image breast microcalcifications. However, new imaging techniques using data acquired with a synthetic-aperture ultrasound system have the potential to significantly improve ultrasound imaging. We recently developed a super-resolution ultrasound imaging method termed the phase-coherent multiple-signal classification (PC-MUSIC). This signal subspace method accounts for the phase response of transducer elements to improve image resolution. In this paper, we investigate the clinical feasibility of our super-resolution ultrasound imaging method for detecting breast microcalcifications. We use our custom-built, real-time synthetic-aperture ultrasound system to acquire breast ultrasound data for 40 patients whose mammograms show the presence of breast microcalcifications. We apply our super-resolution ultrasound imaging method to the patient data, and produce clear images of breast calcifications. Our super-resolution ultrasound PC-MUSIC imaging with synthetic-aperture ultrasound data can provide a new imaging modality for detecting breast microcalcifications in clinic without using ionizing radiation.

Super-Resolution Microscopy: Shedding Light on the Cellular Plasma Membrane.

PubMed

Stone, Matthew B; Shelby, Sarah A; Veatch, Sarah L

2017-06-14

Lipids and the membranes they form are fundamental building blocks of cellular life, and their geometry and chemical properties distinguish membranes from other cellular environments. Collective processes occurring within membranes strongly impact cellular behavior and biochemistry, and understanding these processes presents unique challenges due to the often complex and myriad interactions between membrane components. Super-resolution microscopy offers a significant gain in resolution over traditional optical microscopy, enabling the localization of individual molecules even in densely labeled samples and in cellular and tissue environments. These microscopy techniques have been used to examine the organization and dynamics of plasma membrane components, providing insight into the fundamental interactions that determine membrane functions. Here, we broadly introduce the structure and organization of the mammalian plasma membrane and review recent applications of super-resolution microscopy to the study of membranes. We then highlight some inherent challenges faced when using super-resolution microscopy to study membranes, and we discuss recent technical advancements that promise further improvements to super-resolution microscopy and its application to the plasma membrane.

Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

NASA Astrophysics Data System (ADS)

Petrou, Zisis I.; Xian, Yang; Tian, YingLi

2018-04-01

Estimation of sea ice motion at fine scales is important for a number of regional and local level applications, including modeling of sea ice distribution, ocean-atmosphere and climate dynamics, as well as safe navigation and sea operations. In this study, we propose an optical flow and super-resolution approach to accurately estimate motion from remote sensing images at a higher spatial resolution than the original data. First, an external example learning-based super-resolution method is applied on the original images to generate higher resolution versions. Then, an optical flow approach is applied on the higher resolution images, identifying sparse correspondences and interpolating them to extract a dense motion vector field with continuous values and subpixel accuracies. Our proposed approach is successfully evaluated on passive microwave, optical, and Synthetic Aperture Radar data, proving appropriate for multi-sensor applications and different spatial resolutions. The approach estimates motion with similar or higher accuracy than the original data, while increasing the spatial resolution of up to eight times. In addition, the adopted optical flow component outperforms a state-of-the-art pattern matching method. Overall, the proposed approach results in accurate motion vectors with unprecedented spatial resolutions of up to 1.5 km for passive microwave data covering the entire Arctic and 20 m for radar data, and proves promising for numerous scientific and operational applications.

A Search for Water in a Super-Earth Atmosphere: High-resolution Optical Spectroscopy of 55Cancri e

DOE Office of Scientific and Technical Information (OSTI.GOV)

Esteves, Lisa J.; De Mooij, Ernst J. W.; Watson, Chris

We present the analysis of high-resolution optical spectra of four transits of 55Cnc e, a low-density super-Earth that orbits a nearby Sun-like star in under 18 hr. The inferred bulk density of the planet implies a substantial envelope, which, according to mass–radius relationships, could be either a low-mass extended or a high-mass compact atmosphere. Our observations investigate the latter scenario, with water as the dominant species. We take advantage of the Doppler cross-correlation technique, high-spectral resolution, and the large wavelength coverage of our observations to search for the signature of thousands of optical water absorption lines. Using our observations with HDSmore » on the Subaru telescope and ESPaDOnS on the Canada–France–Hawaii Telescope, we are able to place a 3 σ lower limit of 10 g mol{sup −1} on the mean-molecular weight of 55Cnc e’s water-rich (volume mixing ratio >10%), optically thin atmosphere, which corresponds to an atmospheric scale-height of ∼80 km. Our study marks the first high-spectral resolution search for water in a super-Earth atmosphere, and demonstrates that it is possible to recover known water-vapor absorption signals in a nearby super-Earth atmosphere, using high-resolution transit spectroscopy with current ground-based instruments.« less

Comparison between different thickness umbrella-shaped expandable radiofrequency electrodes (SuperSlim and CoAccess): Experimental and clinical study

PubMed Central

KODA, MASAHIKO; TOKUNAGA, SHIHO; MATONO, TOMOMITSU; SUGIHARA, TAKAAKI; NAGAHARA, TAKAKAZU; MURAWAKI, YOSHIKAZU

2011-01-01

The purpose of the present study was to compare the size and configuration of the ablation zones created by SuperSlim and CoAccess electrodes, using various ablation algorithms in ex vivo bovine liver and in clinical cases. In the experimental study, we ablated explanted bovine liver using 2 types of electrodes and 4 ablation algorithms (combinations of incremental power supply, stepwise expansion and additional low-power ablation) and evaluated the ablation area and time. In the clinical study, we compared the ablation volume and the shape of the ablation zone between both electrodes in 23 hepatocellular carcinoma (HCC) cases with the best algorithm (incremental power supply, stepwise expansion and additional low-power ablation) as derived from the experimental study. In the experimental study, the ablation area and time by the CoAccess electrode were significantly greater compared to those by the SuperSlim electrode for the single-step (algorithm 1, p=0.0209 and 0.0325, respectively) and stepwise expansion algorithms (algorithm 2, p=0.0002 and <0.0001, respectively; algorithm 3, p= 0.006 and 0.0407, respectively). However, differences were not significant for the additional low-power ablation algorithm. In the clinical study, the ablation volume and time in the CoAccess group were significantly larger and longer, respectively, compared to those in the SuperSlim group (p=0.0242 and 0.009, respectively). Round ablation zones were acquired in 91.7% of the CoAccess group, while irregular ablation zones were obtained in 45.5% of the SuperSlim group (p=0.0428). In conclusion, the CoAccess electrode achieves larger and more uniform ablation zones compared with the SuperSlim electrode, though it requires longer ablation times in experimental and clinical studies. PMID:22977647

Airport Traffic Conflict Detection and Resolution Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Jones, Denise R.; Chartrand, Ryan C.; Wilson, Sara R.; Commo, Sean A.; Otero, Sharon D.; Barker, Glover D.

2012-01-01

A conflict detection and resolution (CD&R) concept for the terminal maneuvering area (TMA) was evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. The CD&R concept is being designed to enhance surface situation awareness and provide cockpit alerts of potential conflicts during runway, taxi, and low altitude air-to-air operations. The purpose of the study was to evaluate the performance of aircraft-based CD&R algorithms in the TMA, as a function of surveillance accuracy. This paper gives an overview of the CD&R concept, simulation study, and results. The Next Generation Air Transportation System (NextGen) concept for the year 2025 and beyond envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner [1]. NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and provide an overall system capacity up to three times that of current operating levels. Emerging NextGen operational concepts [2], such as four-dimensional trajectory based airborne and surface operations, equivalent visual operations, and super density arrival and departure operations, require a different approach to air traffic management and as a result, a dramatic shift in the tasks, roles, and responsibilities for the flight deck and air traffic control (ATC) to ensure a safe, sustainable air transportation system.

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Robust video super-resolution with registration efficiency adaptation

NASA Astrophysics Data System (ADS)

Zhang, Xinfeng; Xiong, Ruiqin; Ma, Siwei; Zhang, Li; Gao, Wen

2010-07-01

Super-Resolution (SR) is a technique to construct a high-resolution (HR) frame by fusing a group of low-resolution (LR) frames describing the same scene. The effectiveness of the conventional super-resolution techniques, when applied on video sequences, strongly relies on the efficiency of motion alignment achieved by image registration. Unfortunately, such efficiency is limited by the motion complexity in the video and the capability of adopted motion model. In image regions with severe registration errors, annoying artifacts usually appear in the produced super-resolution video. This paper proposes a robust video super-resolution technique that adapts itself to the spatially-varying registration efficiency. The reliability of each reference pixel is measured by the corresponding registration error and incorporated into the optimization objective function of SR reconstruction. This makes the SR reconstruction highly immune to the registration errors, as outliers with higher registration errors are assigned lower weights in the objective function. In particular, we carefully design a mechanism to assign weights according to registration errors. The proposed superresolution scheme has been tested with various video sequences and experimental results clearly demonstrate the effectiveness of the proposed method.

Super-resolution optical imaging and magnetometry using NV centers in diamond

NASA Astrophysics Data System (ADS)

Jaskula, Jean-Christophe; Trifonov, Alexei; Glenn, David; Bar-Gill, Nir; Walsworth, Ronald

2013-05-01

We report progress done on the development and application of depletion-based techniques for super-resolution (nanoscale) optical imaging and magnetometry using NV centers in diamond. In particulare we are integrating stimulated emission depletion (STED) and ground state depletion (GSD) imaging techniques with advanced pulsed sequences for AC magnetometry. NV centers in diamond do not bleach under optical excitation, have long-lived electronic spin coherence and spin-state-dependent fluorescence, and are not biotoxic. Thus NV-diamond has great potential in quantum science and as a nanoscale magnetic biosensor.

3D high- and super-resolution imaging using single-objective SPIM.

PubMed

Galland, Remi; Grenci, Gianluca; Aravind, Ajay; Viasnoff, Virgile; Studer, Vincent; Sibarita, Jean-Baptiste

2015-07-01

Single-objective selective-plane illumination microscopy (soSPIM) is achieved with micromirrored cavities combined with a laser beam-steering unit installed on a standard inverted microscope. The illumination and detection are done through the same objective. soSPIM can be used with standard sample preparations and features high background rejection and efficient photon collection, allowing for 3D single-molecule-based super-resolution imaging of whole cells or cell aggregates. Using larger mirrors enabled us to broaden the capabilities of our system to image Drosophila embryos.

Watching entangled circular DNA in real time with super-resolution

NASA Astrophysics Data System (ADS)

Jee, Ah-Young; Kim, Hyeongju; Granick, Steve

In this talk, we will show how we unraveled the conformational dynamics of entangled ring-shaped polymers in network, which is one of the most well-known problems in polymer physics, using deep imaging based on super-resolution fluorescence imaging, stimulated emission depletion (STED) microscopy. By using home-written software, we obtained the statistics of each of the hundreds of molecules, mapping out a large statistical distribution. Through inspection we not only found some aspects of the classic understanding of polymers, but some surprising aspects as well.

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

Demosaiced pixel super-resolution in digital holography for multiplexed computational color imaging on-a-chip (Conference Presentation)

NASA Astrophysics Data System (ADS)

Wu, Yichen; Zhang, Yibo; Luo, Wei; Ozcan, Aydogan

2017-03-01

Digital holographic on-chip microscopy achieves large space-bandwidth-products (e.g., >1 billion) by making use of pixel super-resolution techniques. To synthesize a digital holographic color image, one can take three sets of holograms representing the red (R), green (G) and blue (B) parts of the spectrum and digitally combine them to synthesize a color image. The data acquisition efficiency of this sequential illumination process can be improved by 3-fold using wavelength-multiplexed R, G and B illumination that simultaneously illuminates the sample, and using a Bayer color image sensor with known or calibrated transmission spectra to digitally demultiplex these three wavelength channels. This demultiplexing step is conventionally used with interpolation-based Bayer demosaicing methods. However, because the pixels of different color channels on a Bayer image sensor chip are not at the same physical location, conventional interpolation-based demosaicing process generates strong color artifacts, especially at rapidly oscillating hologram fringes, which become even more pronounced through digital wave propagation and phase retrieval processes. Here, we demonstrate that by merging the pixel super-resolution framework into the demultiplexing process, such color artifacts can be greatly suppressed. This novel technique, termed demosaiced pixel super-resolution (D-PSR) for digital holographic imaging, achieves very similar color imaging performance compared to conventional sequential R,G,B illumination, with 3-fold improvement in image acquisition time and data-efficiency. We successfully demonstrated the color imaging performance of this approach by imaging stained Pap smears. The D-PSR technique is broadly applicable to high-throughput, high-resolution digital holographic color microscopy techniques that can be used in resource-limited-settings and point-of-care offices.

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

DTIC Science & Technology

2008-12-12

Image Process., vol. 5, no. 6, pp. 996–1011, Jun. 1996. [7] A. J. Patti, M. I. Sezan, and M. A. Tekalp, “ Superresolution video reconstruction with...computationally efficient image superresolution algorithm,” IEEE Trans. Image Process., vol. 10, no. 4, pp. 573–583, Apr. 2001. [13] M. Elad and Y...pp. 21–36, May 2003. [18] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to superresolution ,” in Proc. SPIE Conf

Correlative super-resolution fluorescence microscopy combined with optical coherence microscopy

NASA Astrophysics Data System (ADS)

Kim, Sungho; Kim, Gyeong Tae; Jang, Soohyun; Shim, Sang-Hee; Bae, Sung Chul

2015-03-01

Recent development of super-resolution fluorescence imaging technique such as stochastic optical reconstruction microscopy (STORM) and photoactived localization microscope (PALM) has brought us beyond the diffraction limits. It allows numerous opportunities in biology because vast amount of formerly obscured molecular structures, due to lack of spatial resolution, now can be directly observed. A drawback of fluorescence imaging, however, is that it lacks complete structural information. For this reason, we have developed a super-resolution multimodal imaging system based on STORM and full-field optical coherence microscopy (FF-OCM). FF-OCM is a type of interferometry systems based on a broadband light source and a bulk Michelson interferometer, which provides label-free and non-invasive visualization of biological samples. The integration between the two systems is simple because both systems use a wide-field illumination scheme and a conventional microscope. This combined imaging system gives us both functional information at a molecular level (~20nm) and structural information at the sub-cellular level (~1μm). For thick samples such as tissue slices, while FF-OCM is readily capable of imaging the 3D architecture, STORM suffer from aberrations and high background fluorescence that substantially degrade the resolution. In order to correct the aberrations in thick tissues, we employed an adaptive optics system in the detection path of the STORM microscope. We used our multimodal system to obtain images on brain tissue samples with structural and functional information.

In situ high temperature microwave microscope for nondestructive detection of surface and sub-surface defects.

PubMed

Wang, Peiyu; Li, Zhencheng; Pei, Yongmao

2018-04-16

An in situ high temperature microwave microscope was built for detecting surface and sub-subsurface structures and defects. This system was heated with a self-designed quartz lamp radiation module, which is capable of heating to 800°C. A line scanning of a metal grating showed a super resolution of 0.5 mm (λ/600) at 1 GHz. In situ scanning detections of surface hole defects on an aluminium plate and a glass fiber reinforced plastic (GFRP) plate were conducted at different high temperatures. A post processing algorithm was proposed to remove the background noises induced by high temperatures and the 3.0 mm-spaced hole defects were clearly resolved. Besides, hexagonal honeycomb lattices were in situ detected and clearly resolved under a 1.0 mm-thick face panel at 20°C and 50°C, respectively. The core wall positions and bonding width were accurately detected and evaluated. In summary, this in situ microwave microscope is feasible and effective in sub-surface detection and super resolution imaging at different high temperatures.

Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang

2018-04-01

Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

Evanescent-Wave Filtering in Images Using Remote Terahertz Structured Illumination

NASA Astrophysics Data System (ADS)

Flammini, M.; Pontecorvo, E.; Giliberti, V.; Rizza, C.; Ciattoni, A.; Ortolani, M.; DelRe, E.

2017-11-01

Imaging with structured illumination allows for the retrieval of subwavelength features of an object by conversion of evanescent waves into propagating waves. In conditions in which the object plane and the structured-illumination plane do not coincide, this conversion process is subject to progressive filtering of the components with high spatial frequency when the distance between the two planes increases, until the diffraction-limited lateral resolution is restored when the distance exceeds the extension of evanescent waves. We study the progressive filtering of evanescent waves by developing a remote super-resolution terahertz imaging system operating at a wavelength λ =1.00 mm , based on a freestanding knife edge and a reflective confocal terahertz microscope. In the images recorded with increasing knife-edge-to-object-plane distance, we observe the transition from a super-resolution of λ /17 ≃60 μ m to the diffraction-limited lateral resolution of Δ x ≃λ expected for our confocal microscope. The extreme nonparaxial conditions are analyzed in detail, exploiting the fact that, in the terahertz frequency range, the knife edge can be positioned at a variable subwavelength distance from the object plane. Electromagnetic simulations of radiation scattering by the knife edge reproduce the experimental super-resolution achieved.

Multi-pulse pumping for far-field super-resolution imaging

NASA Astrophysics Data System (ADS)

Requena, Sebastian; Raut, Sangram; Doan, Hung; Kimball, Joe; Fudala, Rafal; Borejdo, Julian; Gryczynski, Ignacy; Strzhemechny, Yuri; Gryczynski, Zygmunt

2016-02-01

Recently, far-field optical imaging with a resolution significantly beyond diffraction limit has attracted tremendous attention allowing for high resolution imaging in living objects. Various methods have been proposed that are divided in to two basic approaches; deterministic super-resolution like STED or RESOLFT and stochastic super-resolution like PALM or STORM. We propose to achieve super-resolution in far-field fluorescence imaging by the use of controllable (on-demand) bursts of pulses that can change the fluorescence signal of long-lived component over one order of magnitude. We demonstrate that two beads, one labeled with a long-lived dye and another with a short-lived dye, separated by a distance lower than 100 nm can be easily resolved in a single experiment. The proposed method can be used to separate two biological structures in a cell by targeting them with two antibodies labeled with long-lived and short-lived fluorophores.

Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

PubMed Central

Sobieranski, Antonio C; Inci, Fatih; Tekin, H Cumhur; Yuksekkaya, Mehmet; Comunello, Eros; Cobra, Daniel; von Wangenheim, Aldo; Demirci, Utkan

2017-01-01

In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm2. PMID:29657866

Live-cell super-resolution imaging of intrinsically fast moving flagellates

NASA Astrophysics Data System (ADS)

Glogger, M.; Stichler, S.; Subota, I.; Bertlein, S.; Spindler, M.-C.; Teßmar, J.; Groll, J.; Engstler, M.; Fenz, S. F.

2017-02-01

Recent developments in super-resolution microscopy make it possible to resolve structures in biological cells at a spatial resolution of a few nm and observe dynamical processes with a temporal resolution of ms to μs. However, the optimal structural resolution requires repeated illumination cycles and is thus limited to chemically fixed cells. For live cell applications substantial improvement over classical Abbe-limited imaging can already be obtained in adherent or slow moving cells. Nonetheless, a large group of cells are fast moving and thus could not yet be addressed with live cell super-resolution microscopy. These include flagellate pathogens like African trypanosomes, the causative agents of sleeping sickness in humans and nagana in livestock. Here, we present an embedding method based on a in situ forming cytocompatible UV-crosslinked hydrogel. The fast cross-linking hydrogel immobilizes trypanosomes efficiently to allow microscopy on the nanoscale. We characterized both the trypanosomes and the hydrogel with respect to their autofluorescence properties and found them suitable for single-molecule fluorescence microscopy (SMFM). As a proof of principle, SMFM was applied to super-resolve a structure inside the living trypanosome. We present an image of a flagellar axoneme component recorded by using the intrinsic blinking behavior of eYFP. , which features invited work from the best early-career researchers working within the scope of J Phys D. This project is part of the Journal of Physics series’ 50th anniversary celebrations in 2017. Susanne Fenz was selected by the Editorial Board of J Phys D as an Emerging Talent/Leader.

Super-resolution imaging of subcortical white matter using stochastic optical reconstruction microscopy (STORM) and super-resolution optical fluctuation imaging (SOFI)

PubMed Central

Hainsworth, A. H.; Lee, S.; Patel, A.; Poon, W. W.; Knight, A. E.

2018-01-01

Aims The spatial resolution of light microscopy is limited by the wavelength of visible light (the ‘diffraction limit’, approximately 250 nm). Resolution of sub-cellular structures, smaller than this limit, is possible with super resolution methods such as stochastic optical reconstruction microscopy (STORM) and super-resolution optical fluctuation imaging (SOFI). We aimed to resolve subcellular structures (axons, myelin sheaths and astrocytic processes) within intact white matter, using STORM and SOFI. Methods Standard cryostat-cut sections of subcortical white matter from donated human brain tissue and from adult rat and mouse brain were labelled, using standard immunohistochemical markers (neurofilament-H, myelin-associated glycoprotein, glial fibrillary acidic protein, GFAP). Image sequences were processed for STORM (effective pixel size 8–32 nm) and for SOFI (effective pixel size 80 nm). Results In human, rat and mouse, subcortical white matter high-quality images for axonal neurofilaments, myelin sheaths and filamentous astrocytic processes were obtained. In quantitative measurements, STORM consistently underestimated width of axons and astrocyte processes (compared with electron microscopy measurements). SOFI provided more accurate width measurements, though with somewhat lower spatial resolution than STORM. Conclusions Super resolution imaging of intact cryo-cut human brain tissue is feasible. For quantitation, STORM can under-estimate diameters of thin fluorescent objects. SOFI is more robust. The greatest limitation for super-resolution imaging in brain sections is imposed by sample preparation. We anticipate that improved strategies to reduce autofluorescence and to enhance fluorophore performance will enable rapid expansion of this approach. PMID:28696566

Super-resolution imaging of subcortical white matter using stochastic optical reconstruction microscopy (STORM) and super-resolution optical fluctuation imaging (SOFI).

PubMed

Hainsworth, A H; Lee, S; Foot, P; Patel, A; Poon, W W; Knight, A E

2018-06-01

The spatial resolution of light microscopy is limited by the wavelength of visible light (the 'diffraction limit', approximately 250 nm). Resolution of sub-cellular structures, smaller than this limit, is possible with super resolution methods such as stochastic optical reconstruction microscopy (STORM) and super-resolution optical fluctuation imaging (SOFI). We aimed to resolve subcellular structures (axons, myelin sheaths and astrocytic processes) within intact white matter, using STORM and SOFI. Standard cryostat-cut sections of subcortical white matter from donated human brain tissue and from adult rat and mouse brain were labelled, using standard immunohistochemical markers (neurofilament-H, myelin-associated glycoprotein, glial fibrillary acidic protein, GFAP). Image sequences were processed for STORM (effective pixel size 8-32 nm) and for SOFI (effective pixel size 80 nm). In human, rat and mouse, subcortical white matter high-quality images for axonal neurofilaments, myelin sheaths and filamentous astrocytic processes were obtained. In quantitative measurements, STORM consistently underestimated width of axons and astrocyte processes (compared with electron microscopy measurements). SOFI provided more accurate width measurements, though with somewhat lower spatial resolution than STORM. Super resolution imaging of intact cryo-cut human brain tissue is feasible. For quantitation, STORM can under-estimate diameters of thin fluorescent objects. SOFI is more robust. The greatest limitation for super-resolution imaging in brain sections is imposed by sample preparation. We anticipate that improved strategies to reduce autofluorescence and to enhance fluorophore performance will enable rapid expansion of this approach. © 2017 British Neuropathological Society.

Multi-frame super-resolution with quality self-assessment for retinal fundus videos.

PubMed

Köhler, Thomas; Brost, Alexander; Mogalle, Katja; Zhang, Qianyi; Köhler, Christiane; Michelson, Georg; Hornegger, Joachim; Tornow, Ralf P

2014-01-01

This paper proposes a novel super-resolution framework to reconstruct high-resolution fundus images from multiple low-resolution video frames in retinal fundus imaging. Natural eye movements during an examination are used as a cue for super-resolution in a robust maximum a-posteriori scheme. In order to compensate heterogeneous illumination on the fundus, we integrate retrospective illumination correction for photometric registration to the underlying imaging model. Our method utilizes quality self-assessment to provide objective quality scores for reconstructed images as well as to select regularization parameters automatically. In our evaluation on real data acquired from six human subjects with a low-cost video camera, the proposed method achieved considerable enhancements of low-resolution frames and improved noise and sharpness characteristics by 74%. In terms of image analysis, we demonstrate the importance of our method for the improvement of automatic blood vessel segmentation as an example application, where the sensitivity was increased by 13% using super-resolution reconstruction.

Super-resolved terahertz microscopy by knife-edge scan

NASA Astrophysics Data System (ADS)

Giliberti, V.; Flammini, M.; Ciano, C.; Pontecorvo, E.; Del Re, E.; Ortolani, M.

2017-08-01

We present a compact, all solid-state THz confocal microscope operating at 0.30 THz that achieves super-resolution by using the knife-edge scan approach. In the final reconstructed image, a lateral resolution of 60 μm ≍ λ/17 is demonstrated when the knife-edge is deep in the near-field of the sample surface. When the knife-edge is lifted up to λ/4 from the sample surface, a certain degree of super-resolution is maintained with a resolution of 0.4 mm, i.e. more than a factor 2 if compared to the diffraction-limited scheme. The present results open an interesting path towards super-resolved imaging with in-depth information that would be peculiar to THz microscopy systems.

Super-resolution Microscopy in Plant Cell Imaging.

PubMed

Komis, George; Šamajová, Olga; Ovečka, Miroslav; Šamaj, Jozef

2015-12-01

Although the development of super-resolution microscopy methods dates back to 1994, relevant applications in plant cell imaging only started to emerge in 2010. Since then, the principal super-resolution methods, including structured-illumination microscopy (SIM), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and stimulated emission depletion microscopy (STED), have been implemented in plant cell research. However, progress has been limited due to the challenging properties of plant material. Here we summarize the basic principles of existing super-resolution methods and provide examples of applications in plant science. The limitations imposed by the nature of plant material are reviewed and the potential for future applications in plant cell imaging is highlighted. Copyright © 2015 Elsevier Ltd. All rights reserved.

BCD Beam Search: considering suboptimal partial solutions in Bad Clade Deletion supertrees.

PubMed

Fleischauer, Markus; Böcker, Sebastian

2018-01-01

Supertree methods enable the reconstruction of large phylogenies. The supertree problem can be formalized in different ways in order to cope with contradictory information in the input. Some supertree methods are based on encoding the input trees in a matrix; other methods try to find minimum cuts in some graph. Recently, we introduced Bad Clade Deletion (BCD) supertrees which combines the graph-based computation of minimum cuts with optimizing a global objective function on the matrix representation of the input trees. The BCD supertree method has guaranteed polynomial running time and is very swift in practice. The quality of reconstructed supertrees was superior to matrix representation with parsimony (MRP) and usually on par with SuperFine for simulated data; but particularly for biological data, quality of BCD supertrees could not keep up with SuperFine supertrees. Here, we present a beam search extension for the BCD algorithm that keeps alive a constant number of partial solutions in each top-down iteration phase. The guaranteed worst-case running time of the new algorithm is still polynomial in the size of the input. We present an exact and a randomized subroutine to generate suboptimal partial solutions. Both beam search approaches consistently improve supertree quality on all evaluated datasets when keeping 25 suboptimal solutions alive. Supertree quality of the BCD Beam Search algorithm is on par with MRP and SuperFine even for biological data. This is the best performance of a polynomial-time supertree algorithm reported so far.

POX 186: the ultracompact blue compact dwarf galaxy reveals its nature

NASA Astrophysics Data System (ADS)

Doublier, V.; Kunth, D.; Courbin, F.; Magain, P.

2000-01-01

High resolution, ground based R and I band observations of the ultra compact dwarf galaxy POX 186 are presented. The data, obtained with the ESO New Technology Telescope (NTT), are analyzed using a new deconvolution algorithm which allows one to resolve the innermost regions of this stellar-like object into three Super-Star Clusters (SSC). Upper limits to both masses (M ~ 105 Msun) and physical sizes (<=60pc) of the SSCs are set. In addition, and maybe most importantly, extended light emission underlying the compact star-forming region is clearly detected in both bands. The R-I color rules out nebular Hα contamination and is consistent with an old stellar population. This casts doubt on the hypothesis that Blue Compact Dwarf Galaxies (BCDG) are young galaxies. based on observations carried out at NTT in La Silla, operated by the European Southern Observatory, during Director's Discretionary Time.

ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chiswell, S

2009-01-11

Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-timemore » level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.« less

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

PubMed

Choi, Jae-Seok; Kim, Munchurl

2017-03-01

Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower computational complexity when compared with a super-resolution method based on convolutional neural nets (SRCNN15). Compared with the previous SI method that is limited with a scale factor of 2, GLM-SI shows superior performance with average 0.79 dB higher in PSNR, and can be used for scale factors of 3 or higher.

Super-resolved microsphere-assisted Mirau digital holography by oblique illumination

NASA Astrophysics Data System (ADS)

Abbasian, Vahid; Ganjkhani, Yasaman; Akhlaghi, Ehsan A.; Anand, Arun; Javidi, Bahram; Moradi, Ali-Reza

2018-06-01

In this paper, oblique illumination is used to improve the lateral resolution and edge sharpness in microsphere (MS)-assisted Mirau digital holographic microscopy (Mirau-DHM). Abbe showed that tilting the illumination light allows entrance of higher spatial frequencies into the imaging system thus increasing the resolution power. We extended the idea to common-path DHM, based on Mirau objective, toward super-resolved 3D imaging. High magnification Mirau objectives are very expensive and low-magnification ones suffer from low resolution, therefore, any attempt to increase the effective resolution of the system may be of a great interest. We have already demonstrated the effective resolution increasing of a Mirau-DHM system by incorporating a transparent MS within the working distance of the objective. Here, we show that by integrating a MS-assisted Mirau-DHM with the oblique illumination even higher resolutions can be achieved. We have applied the technique for various samples and have shown the increase in the lateral resolution for the both cases of Mirau-DHM with and without the MS.

Initial Results from Radiometer and Polarized Radar-Based Icing Algorithms Compared to In-Situ Data

NASA Technical Reports Server (NTRS)

Serke, David; Reehorst, Andrew L.; King, Michael

2015-01-01

In early 2015, a field campaign was conducted at the NASA Glenn Research Center in Cleveland, Ohio, USA. The purpose of the campaign is to test several prototype algorithms meant to detect the location and severity of in-flight icing (or icing aloft, as opposed to ground icing) within the terminal airspace. Terminal airspace for this project is currently defined as within 25 kilometers horizontal distance of the terminal, which in this instance is Hopkins International Airport in Cleveland. Two new and improved algorithms that utilize ground-based remote sensing instrumentation have been developed and were operated during the field campaign. The first is the 'NASA Icing Remote Sensing System', or NIRSS. The second algorithm is the 'Radar Icing Algorithm', or RadIA. In addition to these algorithms, which were derived from ground-based remote sensors, in-situ icing measurements of the profiles of super-cooled liquid water (SLW) collected with vibrating wire sondes attached to weather balloons produced a comprehensive database for comparison. Key fields from the SLW-sondes include air temperature, humidity and liquid water content, cataloged by time and 3-D location. This work gives an overview of the NIRSS and RadIA products and results are compared to in-situ SLW-sonde data from one icing case study. The location and quantity of super-cooled liquid as measured by the in-situ probes provide a measure of the utility of these prototype hazard-sensing algorithms.

Improved vocal tract reconstruction and modeling using an image super-resolution technique.

PubMed

Zhou, Xinhui; Woo, Jonghye; Stone, Maureen; Prince, Jerry L; Espy-Wilson, Carol Y

2013-06-01

Magnetic resonance imaging has been widely used in speech production research. Often only one image stack (sagittal, axial, or coronal) is used for vocal tract modeling. As a result, complementary information from other available stacks is not utilized. To overcome this, a recently developed super-resolution technique was applied to integrate three orthogonal low-resolution stacks into one isotropic volume. The results on vowels show that the super-resolution volume produces better vocal tract visualization than any of the low-resolution stacks. Its derived area functions generally produce formant predictions closer to the ground truth, particularly for those formants sensitive to area perturbations at constrictions.

Super-Resolution Image Reconstruction by Nonlocal Means Applied to High-Angle Annular Darkfield Scanning Transmission Electron Microscopy (HAADF-STEM)

DTIC Science & Technology

2009-10-06

When talking about superresolution we always mean to recover the level of resolution set by the microscope, but by using a time series of low...on low resolution possibly very noisy data, is not feasible. Thus, standard superresolution concepts as described above that are based on registration

Super-resolution reconstruction of hyperspectral images.

PubMed

Akgun, Toygar; Altunbasak, Yucel; Mersereau, Russell M

2005-11-01

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super-resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super-resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

TH-EF-BRA-11: Feasibility of Super-Resolution Time-Resolved 4DMRI for Multi-Breath Volumetric Motion Simulation in Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G; Zakian, K; Deasy, J

Purpose: To develop a novel super-resolution time-resolved 4DMRI technique to evaluate multi-breath, irregular and complex organ motion without respiratory surrogate for radiotherapy planning. Methods: The super-resolution time-resolved (TR) 4DMRI approach combines a series of low-resolution 3D cine MRI images acquired during free breathing (FB) with a high-resolution breath-hold (BH) 3DMRI via deformable image registration (DIR). Five volunteers participated in the study under an IRB-approved protocol. The 3D cine images with voxel size of 5×5×5 mm{sup 3} at two volumes per second (2Hz) were acquired coronally using a T1 fast field echo sequence, half-scan (0.8) acceleration, and SENSE (3) parallel imaging.more » Phase-encoding was set in the lateral direction to minimize motion artifacts. The BH image with voxel size of 2×2×2 mm{sup 3} was acquired using the same sequence within 10 seconds. A demons-based DIR program was employed to produce super-resolution 2Hz 4DMRI. Registration quality was visually assessed using difference images between TR 4DMRI and 3D cine and quantitatively assessed using average voxel correlation. The fidelity of the 3D cine images was assessed using a gel phantom and a 1D motion platform by comparing mobile and static images. Results: Owing to voxel intensity similarity using the same MRI scanning sequence, accurate DIR between FB and BH images is achieved. The voxel correlations between 3D cine and TR 4DMRI are greater than 0.92 in all cases and the difference images illustrate minimal residual error with little systematic patterns. The 3D cine images of the mobile gel phantom preserve object geometry with minimal scanning artifacts. Conclusion: The super-resolution time-resolved 4DMRI technique has been achieved via DIR, providing a potential solution for multi-breath motion assessment. Accurate DIR mapping has been achieved to map high-resolution BH images to low-resolution FB images, producing 2Hz volumetric high-resolution 4DMRI. Further validation and improvement are still required prior to clinical applications. This study is in part supported by the NIH (U54CA137788/U54CA132378).« less

A state space based approach to localizing single molecules from multi-emitter images.

PubMed

Vahid, Milad R; Chao, Jerry; Ward, E Sally; Ober, Raimund J

2017-01-28

Single molecule super-resolution microscopy is a powerful tool that enables imaging at sub-diffraction-limit resolution. In this technique, subsets of stochastically photoactivated fluorophores are imaged over a sequence of frames and accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Available localization methods typically first determine the regions of the image that contain emitting fluorophores through a process referred to as detection. Then, the locations of the fluorophores are estimated accurately in an estimation step. We propose a novel localization method which combines the detection and estimation steps. The method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix, and determines the locations of intensity peaks in the image as the pole locations of the resulting system. The locations of the most significant peaks correspond to the locations of single molecules in the original image. Although the accuracy of the location estimates is reasonably good, we demonstrate that, by using the estimates as the initial conditions for a maximum likelihood estimator, refined estimates can be obtained that have a standard deviation close to the Cramér-Rao lower bound-based limit of accuracy. We validate our method using both simulated and experimental multi-emitter images.

Super-Joule heating in graphene and silver nanowire network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maize, Kerry; Das, Suprem R.; Sadeque, Sajia

Transistors, sensors, and transparent conductors based on randomly assembled nanowire networks rely on multi-component percolation for unique and distinctive applications in flexible electronics, biochemical sensing, and solar cells. While conduction models for 1-D and 1-D/2-D networks have been developed, typically assuming linear electronic transport and self-heating, the model has not been validated by direct high-resolution characterization of coupled electronic pathways and thermal response. In this letter, we show the occurrence of nonlinear “super-Joule” self-heating at the transport bottlenecks in networks of silver nanowires and silver nanowire/single layer graphene hybrid using high resolution thermoreflectance (TR) imaging. TR images at the microscopicmore » self-heating hotspots within nanowire network and nanowire/graphene hybrid network devices with submicron spatial resolution are used to infer electrical current pathways. The results encourage a fundamental reevaluation of transport models for network-based percolating conductors.« less

Assessing resolution in live cell structured illumination microscopy

NASA Astrophysics Data System (ADS)

Pospíšil, Jakub; Fliegel, Karel; Klíma, Miloš

2017-12-01

Structured Illumination Microscopy (SIM) is a powerful super-resolution technique, which is able to enhance the resolution of optical microscope beyond the Abbe diffraction limit. In the last decade, numerous SIM methods that achieve the resolution of 100 nm in the lateral dimension have been developed. The SIM setups with new high-speed cameras and illumination pattern generators allow rapid acquisition of the live specimen. Therefore, SIM is widely used for investigation of the live structures in molecular and live cell biology. Quantitative evaluation of resolution enhancement in a real sample is essential to describe the efficiency of super-resolution microscopy technique. However, measuring the resolution of a live cell sample is a challenging task. Based on our experimental findings, the widely used Fourier ring correlation (FRC) method does not seem to be well suited for measuring the resolution of SIM live cell video sequences. Therefore, the resolution assessing methods based on Fourier spectrum analysis are often used. We introduce a measure based on circular average power spectral density (PSDca) estimated from a single SIM image (one video frame). PSDca describes the distribution of the power of a signal with respect to its spatial frequency. Spatial resolution corresponds to the cut-off frequency in Fourier space. In order to estimate the cut-off frequency from a noisy signal, we use a spectral subtraction method for noise suppression. In the future, this resolution assessment approach might prove useful also for single-molecule localization microscopy (SMLM) live cell imaging.

Super Resolution Imaging Applied to Scientific Images

DTIC Science & Technology

2007-05-01

norm has found favor in the image restoration community because it allows discontinuities in its solution. As opposed to the L2 norm it does not...Oxford University Press. 31) Malay Kumar Nema , S.Rakshit and S.Chaudhuri,”Edge Model Based High Resolution Image Genration”Indian Conference on...Society of America, vol. 11, no. 2, pp. 572- 579, February 1994 37) M. Nema , S. Rakshit and S. Chaudhuri, ``Edge Model Based High Resolution Image

Image inpainting and super-resolution using non-local recursive deep convolutional network with skip connections

NASA Astrophysics Data System (ADS)

Liu, Miaofeng

2017-07-01

In recent years, deep convolutional neural networks come into use in image inpainting and super-resolution in many fields. Distinct to most of the former methods requiring to know beforehand the local information for corrupted pixels, we propose a 20-depth fully convolutional network to learn an end-to-end mapping a dataset of damaged/ground truth subimage pairs realizing non-local blind inpainting and super-resolution. As there often exist image with huge corruptions or inpainting on a low-resolution image that the existing approaches unable to perform well, we also share parameters in local area of layers to achieve spatial recursion and enlarge the receptive field. To avoid the difficulty of training this deep neural network, skip-connections between symmetric convolutional layers are designed. Experimental results shows that the proposed method outperforms state-of-the-art methods for diverse corrupting and low-resolution conditions, it works excellently when realizing super-resolution and image inpainting simultaneously

Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Guang, E-mail: lig2@mskcc.org; Wei, Jie; Kadbi, Mo

Purpose: To develop and evaluate a super-resolution approach to reconstruct time-resolved 4-dimensional magnetic resonance imaging (TR-4DMRI) with a high spatiotemporal resolution for multi-breathing cycle motion assessment. Methods and Materials: A super-resolution approach was developed to combine fast 3-dimensional (3D) cine MRI with low resolution during free breathing (FB) and high-resolution 3D static MRI during breath hold (BH) using deformable image registration. A T1-weighted, turbo field echo sequence, coronal 3D cine acquisition, partial Fourier approximation, and SENSitivity Encoding parallel acceleration were used. The same MRI pulse sequence, field of view, and acceleration techniques were applied in both FB and BH acquisitions;more » the intensity-based Demons deformable image registration method was used. Under an institutional review board–approved protocol, 7 volunteers were studied with 3D cine FB scan (voxel size: 5 × 5 × 5 mm{sup 3}) at 2 Hz for 40 seconds and a 3D static BH scan (2 × 2 × 2 mm{sup 3}). To examine the image fidelity of 3D cine and super-resolution TR-4DMRI, a mobile gel phantom with multi-internal targets was scanned at 3 speeds and compared with the 3D static image. Image similarity among 3D cine, 4DMRI, and 3D static was evaluated visually using difference image and quantitatively using voxel intensity correlation and Dice index (phantom only). Multi-breathing-cycle waveforms were extracted and compared in both phantom and volunteer images using the 3D cine as the references. Results: Mild imaging artifacts were found in the 3D cine and TR-4DMRI of the mobile gel phantom with a Dice index of >0.95. Among 7 volunteers, the super-resolution TR-4DMRI yielded high voxel-intensity correlation (0.92 ± 0.05) and low voxel-intensity difference (<0.05). The detected motion differences between TR-4DMRI and 3D cine were −0.2 ± 0.5 mm (phantom) and −0.2 ± 1.9 mm (diaphragms). Conclusion: Super-resolution TR-4DMRI has been reconstructed with adequate temporal (2 Hz) and spatial (2 × 2 × 2 mm{sup 3}) resolutions. Further TR-4DMRI characterization and improvement are necessary before clinical applications. Multi-breathing cycles can be examined, providing patient-specific breathing irregularities and motion statistics for future 4D radiation therapy.« less

A high-resolution mid-Pleistocene temperature record from Arctic Lake El'gygytgyn: a 50 kyr super interglacial from MIS 33 to MIS 31?

NASA Astrophysics Data System (ADS)

de Wet, Gregory A.; Castañeda, Isla S.; DeConto, Robert M.; Brigham-Grette, Julie

2016-02-01

Previous periods of extreme warmth in Earth's history are of great interest in light of current and predicted anthropogenic warming. Numerous so called "super interglacial" intervals, with summer temperatures significantly warmer than today, have been identified in the 3.6 million year (Ma) sediment record from Lake El'gygytgyn, northeast Russia. To date, however, a high-resolution paleotemperature reconstruction from any of these super interglacials is lacking. Here we present a paleotemperature reconstruction based on branched glycerol dialkyl glycerol tetraethers (brGDGTs) from Marine Isotope Stages (MIS) 35 to MIS 29, including super interglacial MIS 31. To investigate this period in detail, samples were analyzed with an unprecedented average sample resolution of 500 yrs from MIS 33 to MIS 30. Our results suggest the entire period currently defined as MIS 33-31 (∼1114-1062 kyr BP) was characterized by generally warm and highly variable conditions at the lake, at times out of phase with Northern Hemisphere summer insolation, and that cold "glacial" conditions during MIS 32 lasted only a few thousand years. Close similarities are seen with coeval records from high southern latitudes, supporting the suggestion that the interval from MIS 33 to MIS 31 was an exceptionally long interglacial (Teitler et al., 2015). Based on brGDGT temperatures from Lake El'gygytgyn (this study and unpublished results), warming in the western Arctic during MIS 31 was matched only by MIS 11 during the Pleistocene.

Digital super-resolution holographic data storage based on Hermitian symmetry for achieving high areal density.

PubMed

Nobukawa, Teruyoshi; Nomura, Takanori

2017-01-23

Digital super-resolution holographic data storage based on Hermitian symmetry is proposed to store digital data in a tiny area of a medium. In general, reducing a recording area with an aperture leads to the improvement in the storage capacity of holographic data storage. Conventional holographic data storage systems however have a limitation in reducing a recording area. This limitation is called a Nyquist size. Unlike the conventional systems, our proposed system can overcome the limitation with the help of a digital holographic technique and digital signal processing. Experimental result shows that the proposed system can record and retrieve a hologram in a smaller area than the Nyquist size on the basis of Hermitian symmetry.

Optical Super-Resolution by High-Index Liquid-Immersed Microspheres

DTIC Science & Technology

2012-01-01

the BD without liquid can be achieved using microspheres with small-to-moderate index of refraction such as borosilicate glass (n 1.47), soda lime ...titanate glass microspheres with diameters (D) in the range 2–220 lm and with high refractive index (n 1.9–2.1) can be used for super-resolution...achieving optical super-resolution. It has been demonstrated10 that silica spheres with refractive index (n) about 1.46 and with diame- ters (D) in the

3D single-molecule super-resolution microscopy with a tilted light sheet.

PubMed

Gustavsson, Anna-Karin; Petrov, Petar N; Lee, Maurice Y; Shechtman, Yoav; Moerner, W E

2018-01-09

Tilted light sheet microscopy with 3D point spread functions (TILT3D) combines a novel, tilted light sheet illumination strategy with long axial range point spread functions (PSFs) for low-background, 3D super-localization of single molecules as well as 3D super-resolution imaging in thick cells. Because the axial positions of the single emitters are encoded in the shape of each single-molecule image rather than in the position or thickness of the light sheet, the light sheet need not be extremely thin. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The result is simple and flexible 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validate TILT3D for 3D super-resolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed tetrapod PSFs for fiducial bead tracking and live axial drift correction.

Qdot Labeled Actin Super Resolution Motility Assay Measures Low Duty Cycle Muscle Myosin Step-Size

PubMed Central

Wang, Yihua; Ajtai, Katalin; Burghardt, Thomas P.

2013-01-01

Myosin powers contraction in heart and skeletal muscle and is a leading target for mutations implicated in inheritable muscle diseases. During contraction, myosin transduces ATP free energy into the work of muscle shortening against resisting force. Muscle shortening involves relative sliding of myosin and actin filaments. Skeletal actin filaments were fluorescence labeled with a streptavidin conjugate quantum dot (Qdot) binding biotin-phalloidin on actin. Single Qdot’s were imaged in time with total internal reflection fluorescence microscopy then spatially localized to 1-3 nanometers using a super-resolution algorithm as they translated with actin over a surface coated with skeletal heavy meromyosin (sHMM) or full length β-cardiac myosin (MYH7). Average Qdot-actin velocity matches measurements with rhodamine-phalloidin labeled actin. The sHMM Qdot-actin velocity histogram contains low velocity events corresponding to actin translation in quantized steps of ~5 nm. The MYH7 velocity histogram has quantized steps at 3 and 8 nm in addition to 5 nm, and, larger compliance than sHMM depending on MYH7 surface concentration. Low duty cycle skeletal and cardiac myosin present challenges for a single molecule assay because actomyosin dissociates quickly and the freely moving element diffuses away. The in vitro motility assay has modestly more actomyosin interactions and methylcellulose inhibited diffusion to sustain the complex while preserving a subset of encounters that do not overlap in time on a single actin filament. A single myosin step is isolated in time and space then characterized using super-resolution. The approach provides quick, quantitative, and inexpensive step-size measurement for low duty cycle muscle myosin. PMID:23383646

Robust isotropic super-resolution by maximizing a Laplace posterior for MRI volumes

NASA Astrophysics Data System (ADS)

Han, Xian-Hua; Iwamoto, Yutaro; Shiino, Akihiko; Chen, Yen-Wei

2014-03-01

Magnetic resonance imaging can only acquire volume data with finite resolution due to various factors. In particular, the resolution in one direction (such as the slice direction) is much lower than others (such as the in-plane direction), yielding un-realistic visualizations. This study explores to reconstruct MRI isotropic resolution volumes from three orthogonal scans. This proposed super- resolution reconstruction is formulated as a maximum a posterior (MAP) problem, which relies on the generation model of the acquired scans from the unknown high-resolution volumes. Generally, the deviation ensemble of the reconstructed high-resolution (HR) volume from the available LR ones in the MAP is represented as a Gaussian distribution, which usually results in some noise and artifacts in the reconstructed HR volume. Therefore, this paper investigates a robust super-resolution by formulating the deviation set as a Laplace distribution, which assumes sparsity in the deviation ensemble based on the possible insight of the appeared large values only around some unexpected regions. In addition, in order to achieve reliable HR MRI volume, we integrates the priors such as bilateral total variation (BTV) and non-local mean (NLM) into the proposed MAP framework for suppressing artifacts and enriching visual detail. We validate the proposed robust SR strategy using MRI mouse data with high-definition resolution in two direction and low-resolution in one direction, which are imaged in three orthogonal scans: axial, coronal and sagittal planes. Experiments verifies that the proposed strategy can achieve much better HR MRI volumes than the conventional MAP method even with very high-magnification factor: 10.

Adaptive multiple super fast simulated annealing for stochastic microstructure reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryu, Seun; Lin, Guang; Sun, Xin

2013-01-01

Fast image reconstruction from statistical information is critical in image fusion from multimodality chemical imaging instrumentation to create high resolution image with large domain. Stochastic methods have been used widely in image reconstruction from two point correlation function. The main challenge is to increase the efficiency of reconstruction. A novel simulated annealing method is proposed for fast solution of image reconstruction. Combining the advantage of very fast cooling schedules, dynamic adaption and parallelization, the new simulation annealing algorithm increases the efficiencies by several orders of magnitude, making the large domain image fusion feasible.

Sparse coded image super-resolution using K-SVD trained dictionary based on regularized orthogonal matching pursuit.

PubMed

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2015-01-01

Image super-resolution (SR) plays a vital role in medical imaging that allows a more efficient and effective diagnosis process. Usually, diagnosing is difficult and inaccurate from low-resolution (LR) and noisy images. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps, such as segmentation and registration. Therefore, we propose an efficient sparse coded image SR reconstruction technique using a trained dictionary. We apply a simple and efficient regularized version of orthogonal matching pursuit (ROMP) to seek the coefficients of sparse representation. ROMP has the transparency and greediness of OMP and the robustness of the L1-minization that enhance the dictionary learning process to capture feature descriptors such as oriented edges and contours from complex images like brain MRIs. The sparse coding part of the K-SVD dictionary training procedure is modified by substituting OMP with ROMP. The dictionary update stage allows simultaneously updating an arbitrary number of atoms and vectors of sparse coefficients. In SR reconstruction, ROMP is used to determine the vector of sparse coefficients for the underlying patch. The recovered representations are then applied to the trained dictionary, and finally, an optimization leads to high-resolution output of high-quality. Experimental results demonstrate that the super-resolution reconstruction quality of the proposed scheme is comparatively better than other state-of-the-art schemes.

Tilted Light Sheet Microscopy with 3D Point Spread Functions for Single-Molecule Super-Resolution Imaging in Mammalian Cells.

PubMed

Gustavsson, Anna-Karin; Petrov, Petar N; Lee, Maurice Y; Shechtman, Yoav; Moerner, W E

2018-02-01

To obtain a complete picture of subcellular nanostructures, cells must be imaged with high resolution in all three dimensions (3D). Here, we present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The axial positions of the single molecules are encoded in the shape of the PSF rather than in the position or thickness of the light sheet, and the light sheet can therefore be formed using simple optics. The result is flexible and user-friendly 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validated TILT3D for 3D super-resolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed Tetrapod PSF for fiducial bead tracking and live axial drift correction. We envision TILT3D to become an important tool not only for 3D super-resolution imaging, but also for live whole-cell single-particle and single-molecule tracking.

Motion adaptive Kalman filter for super-resolution

NASA Astrophysics Data System (ADS)

Richter, Martin; Nasse, Fabian; Schröder, Hartmut

2011-01-01

Superresolution is a sophisticated strategy to enhance image quality of both low and high resolution video, performing tasks like artifact reduction, scaling and sharpness enhancement in one algorithm, all of them reconstructing high frequency components (above Nyquist frequency) in some way. Especially recursive superresolution algorithms can fulfill high quality aspects because they control the video output using a feed-back loop and adapt the result in the next iteration. In addition to excellent output quality, temporal recursive methods are very hardware efficient and therefore even attractive for real-time video processing. A very promising approach is the utilization of Kalman filters as proposed by Farsiu et al. Reliable motion estimation is crucial for the performance of superresolution. Therefore, robust global motion models are mainly used, but this also limits the application of superresolution algorithm. Thus, handling sequences with complex object motion is essential for a wider field of application. Hence, this paper proposes improvements by extending the Kalman filter approach using motion adaptive variance estimation and segmentation techniques. Experiments confirm the potential of our proposal for ideal and real video sequences with complex motion and further compare its performance to state-of-the-art methods like trainable filters.

Super-channel oriented routing, spectrum and core assignment under crosstalk limit in spatial division multiplexing elastic optical networks

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Zhu, Ye; Wang, Chunhui; Yu, Xiaosong; Liu, Chuan; Liu, Binglin; Zhang, Jie

2017-07-01

With the capacity increasing in optical networks enabled by spatial division multiplexing (SDM) technology, spatial division multiplexing elastic optical networks (SDM-EONs) attract much attention from both academic and industry. Super-channel is an important type of service provisioning in SDM-EONs. This paper focuses on the issue of super-channel construction in SDM-EONs. Mixed super-channel oriented routing, spectrum and core assignment (MS-RSCA) algorithm is proposed in SDM-EONs considering inter-core crosstalk. Simulation results show that MS-RSCA can improve spectrum resource utilization and reduce blocking probability significantly compared with the baseline RSCA algorithms.

AUC-Maximizing Ensembles through Metalearning.

PubMed

LeDell, Erin; van der Laan, Mark J; Petersen, Maya

2016-05-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

AUC-Maximizing Ensembles through Metalearning

PubMed Central

LeDell, Erin; van der Laan, Mark J.; Peterson, Maya

2016-01-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree. PMID:27227721

A Novel Azimuth Super-Resolution Method by Synthesizing Azimuth Bandwidth of Multiple Tracks of Airborne Stripmap SAR Data

PubMed Central

Wang, Yan; Li, Jingwen; Sun, Bing; Yang, Jian

2016-01-01

Azimuth resolution of airborne stripmap synthetic aperture radar (SAR) is restricted by the azimuth antenna size. Conventionally, a higher azimuth resolution should be achieved by employing alternate modes that steer the beam in azimuth to enlarge the synthetic antenna aperture. However, if a data set of a certain region, consisting of multiple tracks of airborne stripmap SAR data, is available, the azimuth resolution of specific small region of interest (ROI) can be conveniently improved by a novel azimuth super-resolution method as introduced by this paper. The proposed azimuth super-resolution method synthesize the azimuth bandwidth of the data selected from multiple discontinuous tracks and contributes to a magnifier-like function with which the ROI can be further zoomed in with a higher azimuth resolution than that of the original stripmap images. Detailed derivation of the azimuth super-resolution method, including the steps of two-dimensional dechirping, residual video phase (RVP) removal, data stitching and data correction, is provided. The restrictions of the proposed method are also discussed. Lastly, the presented approach is evaluated via both the single- and multi-target computer simulations. PMID:27304959

Accelerating cross-validation with total variation and its application to super-resolution imaging

NASA Astrophysics Data System (ADS)

Obuchi, Tomoyuki; Ikeda, Shiro; Akiyama, Kazunori; Kabashima, Yoshiyuki

2017-12-01

We develop an approximation formula for the cross-validation error (CVE) of a sparse linear regression penalized by ℓ_1-norm and total variation terms, which is based on a perturbative expansion utilizing the largeness of both the data dimensionality and the model. The developed formula allows us to reduce the necessary computational cost of the CVE evaluation significantly. The practicality of the formula is tested through application to simulated black-hole image reconstruction on the event-horizon scale with super resolution. The results demonstrate that our approximation reproduces the CVE values obtained via literally conducted cross-validation with reasonably good precision.

Single image super-resolution via an iterative reproducing kernel Hilbert space method.

PubMed

Deng, Liang-Jian; Guo, Weihong; Huang, Ting-Zhu

2016-11-01

Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.

Long-distance super-resolution imaging assisted by enhanced spatial Fourier transform.

PubMed

Tang, Heng-He; Liu, Pu-Kun

2015-09-07

A new gradient-index (GRIN) lens that can realize enhanced spatial Fourier transform (FT) over optically long distances is demonstrated. By using an anisotropic GRIN metamaterial with hyperbolic dispersion, evanescent wave in free space can be transformed into propagating wave in the metamaterial and then focused outside due to negative-refraction. Both the results based on the ray tracing and the finite element simulation show that the spatial frequency bandwidth of the spatial FT can be extended to 2.7k(0) (k(0) is the wave vector in free space). Furthermore, assisted by the enhanced spatial FT, a new long-distance (in the optical far-field region) super-resolution imaging scheme is also proposed and the super resolved capability of λ/5 (λ is the wavelength in free space) is verified. The work may provide technical support for designing new-type high-speed microscopes with long working distances.

A user's guide to localization-based super-resolution fluorescence imaging.

PubMed

Dempsey, Graham T

2013-01-01

Advances in far-field fluorescence microscopy over the past decade have led to the development of super-resolution imaging techniques that provide more than an order of magnitude improvement in spatial resolution compared to conventional light microscopy. One such approach, called Stochastic Optical Reconstruction Microscopy (STORM) uses the sequential, nanometer-scale localization of individual fluorophores to reconstruct a high-resolution image of a structure of interest. This is an attractive method for biological investigation at the nanoscale due to its relative simplicity, both conceptually and practically in the laboratory. Like most research tools, however, the devil is in the details. The aim of this chapter is to serve as a guide for applying STORM to the study of biological samples. This chapter will discuss considerations for choosing a photoswitchable fluorescent probe, preparing a sample, selecting hardware for data acquisition, and collecting and analyzing data for image reconstruction. Copyright © 2013 Elsevier Inc. All rights reserved.

Repurposing a photosynthetic antenna protein as a super-resolution microscopy label.

PubMed

Barnett, Samuel F H; Hitchcock, Andrew; Mandal, Amit K; Vasilev, Cvetelin; Yuen, Jonathan M; Morby, James; Brindley, Amanda A; Niedzwiedzki, Dariusz M; Bryant, Donald A; Cadby, Ashley J; Holten, Dewey; Hunter, C Neil

2017-12-01

Techniques such as Stochastic Optical Reconstruction Microscopy (STORM) and Structured Illumination Microscopy (SIM) have increased the achievable resolution of optical imaging, but few fluorescent proteins are suitable for super-resolution microscopy, particularly in the far-red and near-infrared emission range. Here we demonstrate the applicability of CpcA, a subunit of the photosynthetic antenna complex in cyanobacteria, for STORM and SIM imaging. The periodicity and width of fabricated nanoarrays of CpcA, with a covalently attached phycoerythrobilin (PEB) or phycocyanobilin (PCB) chromophore, matched the lines in reconstructed STORM images. SIM and STORM reconstructions of Escherichia coli cells harbouring CpcA-labelled cytochrome bd 1 ubiquinol oxidase in the cytoplasmic membrane show that CpcA-PEB and CpcA-PCB are suitable for super-resolution imaging in vivo. The stability, ease of production, small size and brightness of CpcA-PEB and CpcA-PCB demonstrate the potential of this largely unexplored protein family as novel probes for super-resolution microscopy.

KML Super Overlay to WMS Translator

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2007-01-01

This translator is a server-based application that automatically generates KML super overlay configuration files required by Google Earth for map data access via the Open Geospatial Consortium WMS (Web Map Service) standard. The translator uses a set of URL parameters that mirror the WMS parameters as much as possible, and it also can generate a super overlay subdivision of any given area that is only loaded when needed, enabling very large areas of coverage at very high resolutions. It can make almost any dataset available as a WMS service visible and usable in any KML application, without the need to reformat the data.

Graphene-enabled electron microscopy and correlated super-resolution microscopy of wet cells.

PubMed

Wojcik, Michal; Hauser, Margaret; Li, Wan; Moon, Seonah; Xu, Ke

2015-06-11

The application of electron microscopy to hydrated biological samples has been limited by high-vacuum operating conditions. Traditional methods utilize harsh and laborious sample dehydration procedures, often leading to structural artefacts and creating difficulties for correlating results with high-resolution fluorescence microscopy. Here, we utilize graphene, a single-atom-thick carbon meshwork, as the thinnest possible impermeable and conductive membrane to protect animal cells from vacuum, thus enabling high-resolution electron microscopy of wet and untreated whole cells with exceptional ease. Our approach further allows for facile correlative super-resolution and electron microscopy of wet cells directly on the culturing substrate. In particular, individual cytoskeletal actin filaments are resolved in hydrated samples through electron microscopy and well correlated with super-resolution results.

Re-scan confocal microscopy: scanning twice for better resolution.

PubMed

De Luca, Giulia M R; Breedijk, Ronald M P; Brandt, Rick A J; Zeelenberg, Christiaan H C; de Jong, Babette E; Timmermans, Wendy; Azar, Leila Nahidi; Hoebe, Ron A; Stallinga, Sjoerd; Manders, Erik M M

2013-01-01

We present a new super-resolution technique, Re-scan Confocal Microscopy (RCM), based on standard confocal microscopy extended with an optical (re-scanning) unit that projects the image directly on a CCD-camera. This new microscope has improved lateral resolution and strongly improved sensitivity while maintaining the sectioning capability of a standard confocal microscope. This simple technology is typically useful for biological applications where the combination high-resolution and high-sensitivity is required.

STED super-resolution microscopy of clinical paraffin-embedded human rectal cancer tissue.

PubMed

Ilgen, Peter; Stoldt, Stefan; Conradi, Lena-Christin; Wurm, Christian Andreas; Rüschoff, Josef; Ghadimi, B Michael; Liersch, Torsten; Jakobs, Stefan

2014-01-01

Formalin fixed and paraffin-embedded human tissue resected during cancer surgery is indispensable for diagnostic and therapeutic purposes and represents a vast and largely unexploited resource for research. Optical microscopy of such specimen is curtailed by the diffraction-limited resolution of conventional optical microscopy. To overcome this limitation, we used STED super-resolution microscopy enabling optical resolution well below the diffraction barrier. We visualized nanoscale protein distributions in sections of well-annotated paraffin-embedded human rectal cancer tissue stored in a clinical repository. Using antisera against several mitochondrial proteins, STED microscopy revealed distinct sub-mitochondrial protein distributions, suggesting a high level of structural preservation. Analysis of human tissues stored for up to 17 years demonstrated that these samples were still amenable for super-resolution microscopy. STED microscopy of sections of HER2 positive rectal adenocarcinoma revealed details in the surface and intracellular HER2 distribution that were blurred in the corresponding conventional images, demonstrating the potential of super-resolution microscopy to explore the thus far largely untapped nanoscale regime in tissues stored in biorepositories.

STED Super-Resolution Microscopy of Clinical Paraffin-Embedded Human Rectal Cancer Tissue

PubMed Central

Wurm, Christian Andreas; Rüschoff, Josef; Ghadimi, B. Michael; Liersch, Torsten; Jakobs, Stefan

2014-01-01

Formalin fixed and paraffin-embedded human tissue resected during cancer surgery is indispensable for diagnostic and therapeutic purposes and represents a vast and largely unexploited resource for research. Optical microscopy of such specimen is curtailed by the diffraction-limited resolution of conventional optical microscopy. To overcome this limitation, we used STED super-resolution microscopy enabling optical resolution well below the diffraction barrier. We visualized nanoscale protein distributions in sections of well-annotated paraffin-embedded human rectal cancer tissue stored in a clinical repository. Using antisera against several mitochondrial proteins, STED microscopy revealed distinct sub-mitochondrial protein distributions, suggesting a high level of structural preservation. Analysis of human tissues stored for up to 17 years demonstrated that these samples were still amenable for super-resolution microscopy. STED microscopy of sections of HER2 positive rectal adenocarcinoma revealed details in the surface and intracellular HER2 distribution that were blurred in the corresponding conventional images, demonstrating the potential of super-resolution microscopy to explore the thus far largely untapped nanoscale regime in tissues stored in biorepositories. PMID:25025184

Super-Resolution Imaging of the Golgi in Live Cells with a Bio-orthogonal Ceramide Probe**

PubMed Central

Erdmann, Roman S.; Takakura, Hideo; Thompson, Alexander D.; Rivera-Molina, Felix; Allgeyer, Edward S.; Bewersdorf, Joerg; Toomre, Derek K.; Schepartz, Alanna

2014-01-01

We report a lipid-based strategy to visualize Golgi structure and dynamics at super-resolution in live cells. The method is based on two novel reagents: a trans-cyclooctene-containing ceramide lipid (Cer-TCO) and a highly reactive, tetrazine-tagged near-IR dye (SiR-Tz). These reagents assemble via an extremely rapid ‘tetrazine-click’ reaction into Cer-SiR, a highly photostable ‘vital dye’ that enables prolonged live cell imaging of the Golgi apparatus by 3D confocal and STED microscopy. Cer-SiR is non-toxic at concentrations as high as 2 μM and does not perturb the mobility of Golgi-resident enzymes or the traffic of cargo from the endoplasmic reticulum through the Golgi and to the plasma membrane. PMID:25081303

Parsing Stem Cell Lineage Development Using High Content Image Analysis of Epigenetic Spatial Markers.

PubMed

Kim, Joseph J; Moghe, Prabhas V

2018-06-14

This unit describes a protocol for acquiring and analyzing high-content super-resolution images of human stem cell nuclei for the characterization and classification of the cell differentiation paths based on distinct patterns of epigenetic mark organization. Here, we describe the cell culture, immunocytochemical labeling, super-resolution imaging parameters, and MATLAB-based quantitative image analysis approaches for monitoring human mesenchymal stem cells (hMSCs) and human induced pluripotent stem cells (hiPSCs) as the cells differentiate towards various lineages. Although this protocol uses specific cell types as examples, this approach could be easily extended to a variety of cell types and nuclear epigenetic and mechanosensitive biomarkers that are relevant to specific cell developmental scenarios. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

Analyzing Protein Clusters on the Plasma Membrane: Application of Spatial Statistical Analysis Methods on Super-Resolution Microscopy Images.

PubMed

Paparelli, Laura; Corthout, Nikky; Pavie, Benjamin; Annaert, Wim; Munck, Sebastian

2016-01-01

The spatial distribution of proteins within the cell affects their capability to interact with other molecules and directly influences cellular processes and signaling. At the plasma membrane, multiple factors drive protein compartmentalization into specialized functional domains, leading to the formation of clusters in which intermolecule interactions are facilitated. Therefore, quantifying protein distributions is a necessity for understanding their regulation and function. The recent advent of super-resolution microscopy has opened up the possibility of imaging protein distributions at the nanometer scale. In parallel, new spatial analysis methods have been developed to quantify distribution patterns in super-resolution images. In this chapter, we provide an overview of super-resolution microscopy and summarize the factors influencing protein arrangements on the plasma membrane. Finally, we highlight methods for analyzing clusterization of plasma membrane proteins, including examples of their applications.

The 2015 super-resolution microscopy roadmap

NASA Astrophysics Data System (ADS)

Hell, Stefan W.; Sahl, Steffen J.; Bates, Mark; Zhuang, Xiaowei; Heintzmann, Rainer; Booth, Martin J.; Bewersdorf, Joerg; Shtengel, Gleb; Hess, Harald; Tinnefeld, Philip; Honigmann, Alf; Jakobs, Stefan; Testa, Ilaria; Cognet, Laurent; Lounis, Brahim; Ewers, Helge; Davis, Simon J.; Eggeling, Christian; Klenerman, David; Willig, Katrin I.; Vicidomini, Giuseppe; Castello, Marco; Diaspro, Alberto; Cordes, Thorben

2015-11-01

Far-field optical microscopy using focused light is an important tool in a number of scientific disciplines including chemical, (bio)physical and biomedical research, particularly with respect to the study of living cells and organisms. Unfortunately, the applicability of the optical microscope is limited, since the diffraction of light imposes limitations on the spatial resolution of the image. Consequently the details of, for example, cellular protein distributions, can be visualized only to a certain extent. Fortunately, recent years have witnessed the development of ‘super-resolution’ far-field optical microscopy (nanoscopy) techniques such as stimulated emission depletion (STED), ground state depletion (GSD), reversible saturated optical (fluorescence) transitions (RESOLFT), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), structured illumination microscopy (SIM) or saturated structured illumination microscopy (SSIM), all in one way or another addressing the problem of the limited spatial resolution of far-field optical microscopy. While SIM achieves a two-fold improvement in spatial resolution compared to conventional optical microscopy, STED, RESOLFT, PALM/STORM, or SSIM have all gone beyond, pushing the limits of optical image resolution to the nanometer scale. Consequently, all super-resolution techniques open new avenues of biomedical research. Because the field is so young, the potential capabilities of different super-resolution microscopy approaches have yet to be fully explored, and uncertainties remain when considering the best choice of methodology. Thus, even for experts, the road to the future is sometimes shrouded in mist. The super-resolution optical microscopy roadmap of Journal of Physics D: Applied Physics addresses this need for clarity. It provides guidance to the outstanding questions through a collection of short review articles from experts in the field, giving a thorough discussion on the concepts underlying super-resolution optical microscopy, the potential of different approaches, the importance of label optimization (such as reversible photoswitchable proteins) and applications in which these methods will have a significant impact. Mark Bates, Christian Eggeling

Adaptive block online learning target tracking based on super pixel segmentation

NASA Astrophysics Data System (ADS)

Cheng, Yue; Li, Jianzeng

2018-04-01

Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

Hierarchical Object-based Image Analysis approach for classification of sub-meter multispectral imagery in Tanzania

NASA Astrophysics Data System (ADS)

Chung, C.; Nagol, J. R.; Tao, X.; Anand, A.; Dempewolf, J.

2015-12-01

Increasing agricultural production while at the same time preserving the environment has become a challenging task. There is a need for new approaches for use of multi-scale and multi-source remote sensing data as well as ground based measurements for mapping and monitoring crop and ecosystem state to support decision making by governmental and non-governmental organizations for sustainable agricultural development. High resolution sub-meter imagery plays an important role in such an integrative framework of landscape monitoring. It helps link the ground based data to more easily available coarser resolution data, facilitating calibration and validation of derived remote sensing products. Here we present a hierarchical Object Based Image Analysis (OBIA) approach to classify sub-meter imagery. The primary reason for choosing OBIA is to accommodate pixel sizes smaller than the object or class of interest. Especially in non-homogeneous savannah regions of Tanzania, this is an important concern and the traditional pixel based spectral signature approach often fails. Ortho-rectified, calibrated, pan sharpened 0.5 meter resolution data acquired from DigitalGlobe's WorldView-2 satellite sensor was used for this purpose. Multi-scale hierarchical segmentation was performed using multi-resolution segmentation approach to facilitate the use of texture, neighborhood context, and the relationship between super and sub objects for training and classification. eCognition, a commonly used OBIA software program, was used for this purpose. Both decision tree and random forest approaches for classification were tested. The Kappa index agreement for both algorithms surpassed the 85%. The results demonstrate that using hierarchical OBIA can effectively and accurately discriminate classes at even LCCS-3 legend.

All-passive pixel super-resolution of time-stretch imaging

PubMed Central

Chan, Antony C. S.; Ng, Ho-Cheung; Bogaraju, Sharat C. V.; So, Hayden K. H.; Lam, Edmund Y.; Tsia, Kevin K.

2017-01-01

Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate — hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (≈2–5 GSa/s)—more than four times lower than the originally required readout rate (20 GSa/s) — is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time-stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing. PMID:28303936

Serpentine Ultralong Path with Extended Routing (SUPER) High Resolution Traveling Wave Ion Mobility-MS using Structures for Lossless Ion Manipulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Liulin; Webb, Ian K.; Garimella, Sandilya V. B.

Ion mobility (IM) separations have a broad range of analytical applications, but insufficient resolution limits many applications. Here we report on traveling wave (TW) ion mobility (IM) separations in a Serpentine Ultra-long Path with Extended Routing (SUPER) Structures for Lossless Ion Manipulations (SLIM) module in conjunction with mass spectrometry (MS). The extended routing utilized multiple passes was facilitated by the introduction of a lossless ion switch at the end of the ion path that either directed ions to the MS detector or to another pass through the serpentine separation region, providing theoretically unlimited TWIM path lengths. Ions were confined inmore » the SLIM by rf fields in conjunction with a DC guard bias, enabling essentially lossless TW transmission over greatly extended paths (e.g., ~1094 meters over 81 passes through the 13.5 m serpentine path). In this multi-pass SUPER TWIM provided resolution approximately proportional to the square root of the number of passes (or path length). More than 30-fold higher IM resolution for Agilent tuning mix m/z 622 and 922 ions (~340 vs. ~10) was achieved for 40 passes compared to commercially available drift tube IM and other TWIM-based platforms. An initial evaluation of the isomeric sugars Lacto-N-hexaose and Lacto-N-neohexaose showed the isomeric structures to be baseline resolved, and a new conformational feature for Lacto-N-neohexaose was revealed after 9 passes. The new SLIM SUPER high resolution TWIM platform has broad utility in conjunction with MS and is expected to enable a broad range of previously challenging or intractable separations.« less

Super-resolution technique for CW lidar using Fourier transform reordering and Richardson-Lucy deconvolution.

PubMed

Campbell, Joel F; Lin, Bing; Nehrir, Amin R; Harrison, F Wallace; Obland, Michael D

2014-12-15

An interpolation method is described for range measurements of high precision altimetry with repeating intensity modulated continuous wave (IM-CW) lidar waveforms using binary phase shift keying (BPSK), where the range profile is determined by means of a cross-correlation between the digital form of the transmitted signal and the digitized return signal collected by the lidar receiver. This method uses reordering of the array elements in the frequency domain to convert a repeating synthetic pulse signal to single highly interpolated pulse. This is then enhanced further using Richardson-Lucy deconvolution to greatly enhance the resolution of the pulse. We show the sampling resolution and pulse width can be enhanced by about two orders of magnitude using the signal processing algorithms presented, thus breaking the fundamental resolution limit for BPSK modulation of a particular bandwidth and bit rate. We demonstrate the usefulness of this technique for determining cloud and tree canopy thicknesses far beyond this fundamental limit in a lidar not designed for this purpose.

Large scale superres 3D imaging: light-sheet single-molecule localization microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lu, Chieh Han; Chen, Peilin; Chen, Bi-Chang

2017-02-01

Optical imaging techniques provide much important information in understanding life science especially cellular structure and morphology because "seeing is believing". However, the resolution of optical imaging is limited by the diffraction limit, which is discovered by Ernst Abbe, i.e. λ/2(NA) (NA is the numerical aperture of the objective lens). Fluorescence super-resolution microscopic techniques such as Stimulated emission depletion microscopy (STED), Photoactivated localization microscopy (PALM), and Stochastic optical reconstruction microscopy (STORM) are invented to have the capability of seeing biological entities down to molecular level that are smaller than the diffraction limit (around 200-nm in lateral resolution). These techniques do not physically violate the Abbe limit of resolution but exploit the photoluminescence properties and labelling specificity of fluorescence molecules to achieve super-resolution imaging. However, these super-resolution techniques limit most of their applications to the 2D imaging of fixed or dead samples due to the high laser power needed or slow speed for the localization process. Extended from 2D imaging, light sheet microscopy has been proven to have a lot of applications on 3D imaging at much better spatiotemporal resolutions due to its intrinsic optical sectioning and high imaging speed. Herein, we combine the advantage of localization microscopy and light-sheet microscopy to have super-resolved cellular imaging in 3D across large field of view. With high-density labeled spontaneous blinking fluorophore and wide-field detection of light-sheet microscopy, these allow us to construct 3D super-resolution multi-cellular imaging at high speed ( minutes) by light-sheet single-molecule localization microscopy.

Re-scan confocal microscopy: scanning twice for better resolution

PubMed Central

De Luca, Giulia M.R.; Breedijk, Ronald M.P.; Brandt, Rick A.J.; Zeelenberg, Christiaan H.C.; de Jong, Babette E.; Timmermans, Wendy; Azar, Leila Nahidi; Hoebe, Ron A.; Stallinga, Sjoerd; Manders, Erik M.M.

2013-01-01

We present a new super-resolution technique, Re-scan Confocal Microscopy (RCM), based on standard confocal microscopy extended with an optical (re-scanning) unit that projects the image directly on a CCD-camera. This new microscope has improved lateral resolution and strongly improved sensitivity while maintaining the sectioning capability of a standard confocal microscope. This simple technology is typically useful for biological applications where the combination high-resolution and high-sensitivity is required. PMID:24298422

Diverse protocols for correlative super-resolution fluorescence imaging and electron microscopy of chemically fixed samples

PubMed Central

Kopek, Benjamin G.; Paez-Segala, Maria G.; Shtengel, Gleb; Sochacki, Kem A.; Sun, Mei G.; Wang, Yalin; Xu, C. Shan; van Engelenburg, Schuyler B.; Taraska, Justin W.; Looger, Loren L.; Hess, Harald F.

2017-01-01

Our groups have recently developed related approaches for sample preparation for super-resolution imaging within endogenous cellular environments using correlative light and electron microscopy (CLEM). Four distinct techniques for preparing and acquiring super-resolution CLEM datasets on aldehyde-fixed specimens are provided, including Tokuyasu cryosectioning, whole-cell mount, cell unroofing and platinum replication, and resin embedding and sectioning. Choice of the best protocol for a given application depends on a number of criteria that are discussed in detail. Tokuyasu cryosectioning is relatively rapid but is limited to small, delicate specimens. Whole-cell mount has the simplest sample preparation but is restricted to surface structures. Cell unroofing and platinum replica creates high-contrast, 3-dimensional images of the cytoplasmic surface of the plasma membrane, but is more challenging than whole-cell mount. Resin embedding permits serial sectioning of large samples, but is limited to osmium-resistant probes, and is technically difficult. Expected results from these protocols include super-resolution localization (~10–50 nm) of fluorescent targets within the context of electron microscopy ultrastructure, which can help address cell biological questions. These protocols can be completed in 2–7 days, are compatible with a number of super-resolution imaging protocols, and are broadly applicable across biology. PMID:28384138

Super-resolution for everybody: An image processing workflow to obtain high-resolution images with a standard confocal microscope.

PubMed

Lam, France; Cladière, Damien; Guillaume, Cyndélia; Wassmann, Katja; Bolte, Susanne

2017-02-15

In the presented work we aimed at improving confocal imaging to obtain highest possible resolution in thick biological samples, such as the mouse oocyte. We therefore developed an image processing workflow that allows improving the lateral and axial resolution of a standard confocal microscope. Our workflow comprises refractive index matching, the optimization of microscope hardware parameters and image restoration by deconvolution. We compare two different deconvolution algorithms, evaluate the necessity of denoising and establish the optimal image restoration procedure. We validate our workflow by imaging sub resolution fluorescent beads and measuring the maximum lateral and axial resolution of the confocal system. Subsequently, we apply the parameters to the imaging and data restoration of fluorescently labelled meiotic spindles of mouse oocytes. We measure a resolution increase of approximately 2-fold in the lateral and 3-fold in the axial direction throughout a depth of 60μm. This demonstrates that with our optimized workflow we reach a resolution that is comparable to 3D-SIM-imaging, but with better depth penetration for confocal images of beads and the biological sample. Copyright © 2016 Elsevier Inc. All rights reserved.

A two-step super-Gaussian independent component analysis approach for fMRI data.

PubMed

Ge, Ruiyang; Yao, Li; Zhang, Hang; Long, Zhiying

2015-09-01

Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that incorporates the sparse prior of the sources into the ICA model. 2SGICA uses the super-Gaussian ICA (SGICA) algorithm that is based on a simplified Lewicki-Sejnowski's model to obtain the initial source estimate in the first step. Using a kernel estimator technique, the source density is acquired and fitted to the Laplacian function based on the initial source estimates. The fitted Laplacian prior is used for each source at the second SGICA step. Moreover, the automatic target generation process for initial value generation is used in 2SGICA to guarantee the stability of the algorithm. An adaptive step size selection criterion is also implemented in the proposed algorithm. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of 2SGICA and made a performance comparison between InfomaxICA, FastICA, mean field ICA (MFICA) with Laplacian prior, sparse online dictionary learning (ODL), SGICA and 2SGICA. Both simulated and real fMRI experiments showed that the 2SGICA was most robust to noises, and had the best spatial detection power and the time course estimation among the six methods. Copyright © 2015. Published by Elsevier Inc.

Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm.

PubMed

Angelis, G I; Reader, A J; Markiewicz, P J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2013-08-07

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.

Point target detection utilizing super-resolution strategy for infrared scanning oversampling system

NASA Astrophysics Data System (ADS)

Wang, Longguang; Lin, Zaiping; Deng, Xinpu; An, Wei

2017-11-01

To improve the resolution of remote sensing infrared images, infrared scanning oversampling system is employed with information amount quadrupled, which contributes to the target detection. Generally the image data from double-line detector of infrared scanning oversampling system is shuffled to a whole oversampled image to be post-processed, whereas the aliasing between neighboring pixels leads to image degradation with a great impact on target detection. This paper formulates a point target detection method utilizing super-resolution (SR) strategy concerning infrared scanning oversampling system, with an accelerated SR strategy proposed to realize fast de-aliasing of the oversampled image and an adaptive MRF-based regularization designed to achieve the preserving and aggregation of target energy. Extensive experiments demonstrate the superior detection performance, robustness and efficiency of the proposed method compared with other state-of-the-art approaches.

An implementation of super-encryption using RC4A and MDTM cipher algorithms for securing PDF Files on android

NASA Astrophysics Data System (ADS)

Budiman, M. A.; Rachmawati, D.; Parlindungan, M. R.

2018-03-01

MDTM is a classical symmetric cryptographic algorithm. As with other classical algorithms, the MDTM Cipher algorithm is easy to implement but it is less secure compared to modern symmetric algorithms. In order to make it more secure, a stream cipher RC4A is added and thus the cryptosystem becomes super encryption. In this process, plaintexts derived from PDFs are firstly encrypted with the MDTM Cipher algorithm and are encrypted once more with the RC4A algorithm. The test results show that the value of complexity is Θ(n2) and the running time is linearly directly proportional to the length of plaintext characters and the keys entered.

Super-resolution optical microscopy for studying membrane structure and dynamics.

PubMed

Sezgin, Erdinc

2017-07-12

Investigation of cell membrane structure and dynamics requires high spatial and temporal resolution. The spatial resolution of conventional light microscopy is limited due to the diffraction of light. However, recent developments in microscopy enabled us to access the nano-scale regime spatially, thus to elucidate the nanoscopic structures in the cellular membranes. In this review, we will explain the resolution limit, address the working principles of the most commonly used super-resolution microscopy techniques and summarise their recent applications in the biomembrane field.

A general strategy for developing cell-permeable photo-modulatable organic fluorescent probes for live-cell super-resolution imaging.

PubMed

Pan, Deng; Hu, Zhe; Qiu, Fengwu; Huang, Zhen-Li; Ma, Yilong; Wang, Yina; Qin, Lingsong; Zhang, Zhihong; Zeng, Shaoqun; Zhang, Yu-Hui

2014-11-20

Single-molecule localization microscopy (SMLM) achieves super-resolution imaging beyond the diffraction limit but critically relies on the use of photo-modulatable fluorescent probes. Here we report a general strategy for constructing cell-permeable photo-modulatable organic fluorescent probes for live-cell SMLM by exploiting the remarkable cytosolic delivery ability of a cell-penetrating peptide (rR)3R2. We develop photo-modulatable organic fluorescent probes consisting of a (rR)3R2 peptide coupled to a cell-impermeable organic fluorophore and a recognition unit. Our results indicate that these organic probes are not only cell permeable but can also specifically and directly label endogenous targeted proteins. Using the probes, we obtain super-resolution images of lysosomes and endogenous F-actin under physiological conditions. We resolve the dynamics of F-actin with 10 s temporal resolution in live cells and discern fine F-actin structures with diameters of ~80 nm. These results open up new avenues in the design of fluorescent probes for live-cell super-resolution imaging.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution

NASA Astrophysics Data System (ADS)

Luo, Shouhua; Shen, Tao; Sun, Yi; Li, Jing; Li, Guang; Tang, Xiangyang

2018-04-01

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

3D range-gated super-resolution imaging based on stereo matching for moving platforms and targets

NASA Astrophysics Data System (ADS)

Sun, Liang; Wang, Xinwei; Zhou, Yan

2017-11-01

3D range-gated superresolution imaging is a novel 3D reconstruction technique for target detection and recognition with good real-time performance. However, for moving targets or platforms such as airborne, shipborne, remote operated vehicle and autonomous vehicle, 3D reconstruction has a large error or failure. In order to overcome this drawback, we propose a method of stereo matching for 3D range-gated superresolution reconstruction algorithm. In experiment, the target is a doll of Mario with a height of 38cm at the location of 34m, and we obtain two successive frame images of the Mario. To confirm our method is effective, we transform the original images with translation, rotation, scale and perspective, respectively. The experimental result shows that our method has a good result of 3D reconstruction for moving targets or platforms.

Design and analysis of adaptive Super-Twisting sliding mode control for a microgyroscope.

PubMed

Feng, Zhilin; Fei, Juntao

2018-01-01

This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

Breast Microcalcification Detection Using Super-Resolution Ultrasound Image Reconstruction

DTIC Science & Technology

2010-09-01

microcalcifications often occur as one of two types: calcium oxalate dihydrate or calcium hydroxyapatite. Their sizes range approximately from 0.1 mm to 0.5 mm...super-resolution imaging, ultrasound imaging, wave equation. 1. INTRODUCTION Microcalcifications, tiny specks of mineral deposits ( calcium ), are the

An Off-Grid Turbo Channel Estimation Algorithm for Millimeter Wave Communications.

PubMed

Han, Lingyi; Peng, Yuexing; Wang, Peng; Li, Yonghui

2016-09-22

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly.

Quantum dot immunocytochemical localization of somatostatin in somatostatinoma by Widefield Epifluorescence, super-resolution light, and immunoelectron microscopy.

PubMed

Killingsworth, Murray C; Lai, Ken; Wu, Xiaojuan; Yong, Jim L C; Lee, C Soon

2012-11-01

Quantum dot nanocrystal probes (QDs) have been used for detection of somatostatin hormone in secretory granules of somatostatinoma tumor cells by immunofluorescence light microscopy, super-resolution light microscopy, and immunoelectron microscopy. Immunostaining for all modalities was done using sections taken from an epoxy resin-embedded tissue specimen and a similar labeling protocol. This approach allowed assessment of labeling at light microscopy level before examination at super-resolution and electron microscopy level and was a significant aid in interpretation. Etching of ultrathin sections with saturated sodium metaperiodate was a critical step presumably able to retrieve some tissue antigenicity masked by processing in epoxy resin. Immunofluorescence microscopy of QD-immunolabeled sections showed somatostatin hormone localization in cytoplasmic granules. Some variable staining of tumor gland-like structures appeared related to granule maturity and dispersal of granule contents within the tumor cell cytoplasm. Super-resolution light microscopy demonstrated localization of somatostatin within individual secretory granules to be heterogeneous, and this staining pattern was confirmed by immunoelectron microscopy.

Identification and super-resolution imaging of ligand-activated receptor dimers in live cells

NASA Astrophysics Data System (ADS)

Winckler, Pascale; Lartigue, Lydia; Giannone, Gregory; de Giorgi, Francesca; Ichas, François; Sibarita, Jean-Baptiste; Lounis, Brahim; Cognet, Laurent

2013-08-01

Molecular interactions are key to many chemical and biological processes like protein function. In many signaling processes they occur in sub-cellular areas displaying nanoscale organizations and involving molecular assemblies. The nanometric dimensions and the dynamic nature of the interactions make their investigations complex in live cells. While super-resolution fluorescence microscopies offer live-cell molecular imaging with sub-wavelength resolutions, they lack specificity for distinguishing interacting molecule populations. Here we combine super-resolution microscopy and single-molecule Förster Resonance Energy Transfer (FRET) to identify dimers of receptors induced by ligand binding and provide super-resolved images of their membrane distribution in live cells. By developing a two-color universal-Point-Accumulation-In-the-Nanoscale-Topography (uPAINT) method, dimers of epidermal growth factor receptors (EGFR) activated by EGF are studied at ultra-high densities, revealing preferential cell-edge sub-localization. This methodology which is specifically devoted to the study of molecules in interaction, may find other applications in biological systems where understanding of molecular organization is crucial.

Quantum Dot Immunocytochemical Localization of Somatostatin in Somatostatinoma by Widefield Epifluorescence, Super-resolution Light, and Immunoelectron Microscopy

PubMed Central

Lai, Ken; Wu, Xiaojuan; Yong, Jim L. C.; Lee, C. Soon

2012-01-01

Quantum dot nanocrystal probes (QDs) have been used for detection of somatostatin hormone in secretory granules of somatostatinoma tumor cells by immunofluorescence light microscopy, super-resolution light microscopy, and immunoelectron microscopy. Immunostaining for all modalities was done using sections taken from an epoxy resin-embedded tissue specimen and a similar labeling protocol. This approach allowed assessment of labeling at light microscopy level before examination at super-resolution and electron microscopy level and was a significant aid in interpretation. Etching of ultrathin sections with saturated sodium metaperiodate was a critical step presumably able to retrieve some tissue antigenicity masked by processing in epoxy resin. Immunofluorescence microscopy of QD-immunolabeled sections showed somatostatin hormone localization in cytoplasmic granules. Some variable staining of tumor gland-like structures appeared related to granule maturity and dispersal of granule contents within the tumor cell cytoplasm. Super-resolution light microscopy demonstrated localization of somatostatin within individual secretory granules to be heterogeneous, and this staining pattern was confirmed by immunoelectron microscopy. PMID:22899862

Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

NASA Astrophysics Data System (ADS)

Chu, Zhigang; Yang, Yang; Shen, Linbang

2017-05-01

Functional delay and sum (FDAS) is a novel beamforming algorithm introduced for the three-dimensional (3D) acoustic source identification with solid spherical microphone arrays. Being capable of offering significantly attenuated sidelobes with a fast speed, the algorithm promises to play an important role in interior acoustic source identification. However, it presents some intrinsic imperfections, specifically poor spatial resolution and low quantification accuracy. This paper focuses on conquering these imperfections by ridge detection (RD) and deconvolution approach for the mapping of acoustic sources (DAMAS). The suggested methods are referred to as FDAS+RD and FDAS+RD+DAMAS. Both computer simulations and experiments are utilized to validate their effects. Several interesting conclusions have emerged: (1) FDAS+RD and FDAS+RD+DAMAS both can dramatically ameliorate FDAS's spatial resolution and at the same time inherit its advantages. (2) Compared to the conventional DAMAS, FDAS+RD+DAMAS enjoys the same super spatial resolution, stronger sidelobe attenuation capability and more than two hundred times faster speed. (3) FDAS+RD+DAMAS can effectively conquer FDAS's low quantification accuracy. Whether the focus distance is equal to the distance from the source to the array center or not, it can quantify the source average pressure contribution accurately. This study will be of great significance to the accurate and quick localization and quantification of acoustic sources in cabin environments.

Fundamental limits of reconstruction-based superresolution algorithms under local translation.

PubMed

Lin, Zhouchen; Shum, Heung-Yeung

2004-01-01

Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for superresolution?" In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.

Demosaicking for full motion video 9-band SWIR sensor

NASA Astrophysics Data System (ADS)

Kanaev, Andrey V.; Rawhouser, Marjorie; Kutteruf, Mary R.; Yetzbacher, Michael K.; DePrenger, Michael J.; Novak, Kyle M.; Miller, Corey A.; Miller, Christopher W.

2014-05-01

Short wave infrared (SWIR) spectral imaging systems are vital for Intelligence, Surveillance, and Reconnaissance (ISR) applications because of their abilities to autonomously detect targets and classify materials. Typically the spectral imagers are incapable of providing Full Motion Video (FMV) because of their reliance on line scanning. We enable FMV capability for a SWIR multi-spectral camera by creating a repeating pattern of 3x3 spectral filters on a staring focal plane array (FPA). In this paper we present the imagery from an FMV SWIR camera with nine discrete bands and discuss image processing algorithms necessary for its operation. The main task of image processing in this case is demosaicking of the spectral bands i.e. reconstructing full spectral images with original FPA resolution from spatially subsampled and incomplete spectral data acquired with the choice of filter array pattern. To the best of author's knowledge, the demosaicking algorithms for nine or more equally sampled bands have not been reported before. Moreover all existing algorithms developed for demosaicking visible color filter arrays with less than nine colors assume either certain relationship between the visible colors, which are not valid for SWIR imaging, or presence of one color band with higher sampling rate compared to the rest of the bands, which does not conform to our spectral filter pattern. We will discuss and present results for two novel approaches to demosaicking: interpolation using multi-band edge information and application of multi-frame super-resolution to a single frame resolution enhancement of multi-spectral spatially multiplexed images.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

PubMed Central

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-01-01

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min−1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics. PMID:27991512

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

NASA Astrophysics Data System (ADS)

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-12-01

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min-1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions.

PubMed

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-12-19

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min -1 . The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

SuperHERO: Design of a New Hard X-Ray Focusing Telescope

NASA Technical Reports Server (NTRS)

Gaskin, Jessica; Elsner, Ronald; Ramsey, Brian; Wilson-Hodge, Colleen; Tennant, Allyn; Christe, Steven; Shih, Albert; Kiranmayee, Kilaru; Swartz, Douglas; Seller, Paul;

Tilted light sheet microscopy with 3D point spread functions for single-molecule super-resolution imaging in mammalian cells

NASA Astrophysics Data System (ADS)

Gustavsson, Anna-Karin; Petrov, Petar N.; Lee, Maurice Y.; Shechtman, Yoav; Moerner, W. E.

2018-02-01

To obtain a complete picture of subcellular nanostructures, cells must be imaged with high resolution in all three dimensions (3D). Here, we present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The axial positions of the single molecules are encoded in the shape of the PSF rather than in the position or thickness of the light sheet, and the light sheet can therefore be formed using simple optics. The result is flexible and user-friendly 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validated TILT3D for 3D superresolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed Tetrapod PSF for fiducial bead tracking and live axial drift correction. We envision TILT3D to become an important tool not only for 3D super-resolution imaging, but also for live whole-cell single-particle and single-molecule tracking.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balakin, D. A.; Belinsky, A. V., E-mail: belinsky@inbox.ru

Images formed by light with suppressed photon fluctuations are interesting objects for studies with the aim of increasing their limiting information capacity and quality. This light in the sub-Poisson state can be prepared in a resonator filled with a medium with Kerr nonlinearity, in which self-phase modulation takes place. Spatially and temporally multimode light beams are studied and the production of spatial frequency spectra of suppressed photon fluctuations is described. The efficient operation regimes of the system are found. A particular schematic solution is described, which allows one to realize the potential possibilities laid in the formation of the squeezedmore » states of light to a maximum degree during self-phase modulation in a resonator for the maximal suppression of amplitude quantum noises upon two-dimensional imaging. The efficiency of using light with suppressed quantum fluctuations for computer image processing is studied. An algorithm is described for interpreting measurements for increasing the resolution with respect to the geometrical resolution. A mathematical model that characterizes the measurement scheme is constructed and the problem of the image reconstruction is solved. The algorithm for the interpretation of images is verified. Conditions are found for the efficient application of sub-Poisson light for super-resolution imaging. It is found that the image should have a low contrast and be maximally transparent.« less

Inferring Biological Structures from Super-Resolution Single Molecule Images Using Generative Models

PubMed Central

Maji, Suvrajit; Bruchez, Marcel P.

2012-01-01

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information. PMID:22629348

Single image super-resolution based on approximated Heaviside functions and iterative refinement

PubMed Central

Wang, Xin-Yu; Huang, Ting-Zhu; Deng, Liang-Jian

2018-01-01

One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as “smooth” and “non-smooth”, describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We now introduce a new method in which the binary classification of image components is extended to different degrees of smoothness and non-smoothness, these components being represented by various classes of AHFs. Taking into account the sparsity of the non-smooth components, their coefficients are l1 regularized. In addition, to pick up more image details, the new method uses an iterative refinement for the residuals between the original low-resolution input and the downsampled resulting image. Experimental results showed that the new method is superior to the original AHF method and to four other published methods. PMID:29329298

Long Time-lapse Nanoscopy with Spontaneously Blinking Membrane Probes

PubMed Central

Takakura, Hideo; Zhang, Yongdeng; Erdmann, Roman S.; Thompson, Alexander D.; Lin, Yu; McNellis, Brian; Rivera-Molina, Felix; Uno, Shin-nosuke; Kamiya, Mako; Urano, Yasuteru; Rothman, James E.; Bewersdorf, Joerg; Schepartz, Alanna; Toomre, Derek

2017-01-01

Long time-lapse, diffraction-unlimited super-resolution imaging of cellular structures and organelles in living cells is highly challenging, as it requires dense labeling, bright, highly photostable dyes, and non-toxic conditions. We developed a set of high-density, environment-sensitive (HIDE) membrane probes based on HMSiR that assemble in situ and enable long time-lapse, live cell nanoscopy of discrete cellular structures and organelles with high spatio-temporal resolution. HIDE-enabled nanoscopy movies are up to 50x longer than movies obtained with labeled proteins, reveal the 2D dynamics of the mitochondria, plasma membrane, and filopodia, and the 2D and 3D dynamics of the endoplasmic reticulum in living cells. These new HIDE probes also facilitate the acquisition of live cell, two-color, super-resolution images, greatly expanding the utility of nanoscopy to visualize processes and structures in living cells. PMID:28671662

Sparse signal representation and its applications in ultrasonic NDE.

PubMed

Zhang, Guang-Ming; Zhang, Cheng-Zhong; Harvey, David M

2012-03-01

Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration. Copyright © 2011 Elsevier B.V. All rights reserved.

Super-resolution with an SLM and two intensity images

NASA Astrophysics Data System (ADS)

Alcalá Ochoa, Noé; de León, Y. Ponce

2018-06-01

It is reported a method which may simplify the optical setups used to achieve super-resolution through the amplitude multiplication of two waves. For this end we decompose a super-resolving pupil into two complex masks and with the aid of a Spatial Light Modulator (LCoS) we obtain two intensity images that are subtracted. With this proposal, the traditional experimental optical setups are considerably simplified, with the additional benefit that different masks can be utilized without needing to perform the setup alignment each time.

Contact microspherical nanoscopy: from fundamentals to biomedical applications

NASA Astrophysics Data System (ADS)

Astratov, V. N.; Maslov, A. V.; Brettin, A.; Blanchette, K. F.; Nesmelov, Y. E.; Limberopoulos, N. I.; Walker, D. E.; Urbas, A. M.

2017-02-01

The mechanisms of super-resolution imaging by contact microspherical or microcylindrical nanoscopy remain an enigmatic question since these lenses neither have an ability to amplify the near-fields like in the case of far-field superlens, nor they have a hyperbolic dispersion similar to hyperlenses. In this work, we present results along two lines. First, we performed numerical modeling of super-resolution properties of two-dimensional (2-D) circular lens in the limit of wavelength-scale diameters, λ <= D <= 2λ, and relatively high indices of refraction, n=2. Our preliminary results on imaging point dipoles indicate that the resolution is generally close to λ/4 however on resonance with whispering gallery modes it may be slightly higher. Second, experimentally, we used actin protein filaments for the resolution quantification in microspherical nanoscopy. The critical feature of our approach is based on using arrayed cladding layer with strong localized surface plasmon resonances. This layer is used for enhancing plasmonic near-field illumination of our objects. In combination with the magnification of virtual image, this technique resulted in the lateral resolution of actin protein filaments on the order of λ/7.

Wide field of view 3D label-free super-resolution imaging

NASA Astrophysics Data System (ADS)

Nolvi, Anton; Laidmäe, Ivo; Maconi, Göran; Heinämäki, Jyrki; Hæggström, Edward; Kassamakov, Ivan

2018-02-01

Recently, 3D label-free super-resolution profilers based on microsphere-assisted scanning white light interferometry were introduced having vertical resolution of few angstroms (Å) and a lateral resolution approaching 100 nm. However, the use of a single microsphere to generate the photonic nanojet (PNJ) limits their field of view. We overcome this limitation by using polymer microfibers to generate the PNJ. This increases the field of view by order of magnitude in comparison to the previously developed solutions while still resolving sub 100 nm features laterally and keeping the vertical resolution in 1nm range. To validate the capabilities of our system we used a recordable Blu-ray disc as a sample. It features a grooved surface topology with heights in the range of 20 nm and with distinguishable sub 100 nm lateral features that are unresolvable by diffraction limited optics. We achieved agreement between all three measurement devices across lateral and vertical dimensions. The field of view of our instrument was 110 μm by 2 μm and the imaging time was a couple of seconds.

Microsphere-assisted super-resolution imaging with enlarged numerical aperture by semi-immersion

NASA Astrophysics Data System (ADS)

Wang, Fengge; Yang, Songlin; Ma, Huifeng; Shen, Ping; Wei, Nan; Wang, Meng; Xia, Yang; Deng, Yun; Ye, Yong-Hong

2018-01-01

Microsphere-assisted imaging is an extraordinary simple technology that can obtain optical super-resolution under white-light illumination. Here, we introduce a method to improve the resolution of a microsphere lens by increasing its numerical aperture. In our proposed structure, BaTiO3 glass (BTG) microsphere lenses are semi-immersed in a S1805 layer with a refractive index of 1.65, and then, the semi-immersed microspheres are fully embedded in an elastomer with an index of 1.4. We experimentally demonstrate that this structure, in combination with a conventional optical microscope, can clearly resolve a two-dimensional 200-nm-diameter hexagonally close-packed (hcp) silica microsphere array. On the contrary, the widely used structure where BTG microsphere lenses are fully immersed in a liquid or elastomer cannot even resolve a 250-nm-diameter hcp silica microsphere array. The improvement in resolution through the proposed structure is due to an increase in the effective numerical aperture by semi-immersing BTG microsphere lenses in a high-refractive-index S1805 layer. Our results will inform on the design of microsphere-based high-resolution imaging systems.

Correlative Stochastic Optical Reconstruction Microscopy and Electron Microscopy

PubMed Central

Kim, Doory; Deerinck, Thomas J.; Sigal, Yaron M.; Babcock, Hazen P.; Ellisman, Mark H.; Zhuang, Xiaowei

2015-01-01

Correlative fluorescence light microscopy and electron microscopy allows the imaging of spatial distributions of specific biomolecules in the context of cellular ultrastructure. Recent development of super-resolution fluorescence microscopy allows the location of molecules to be determined with nanometer-scale spatial resolution. However, correlative super-resolution fluorescence microscopy and electron microscopy (EM) still remains challenging because the optimal specimen preparation and imaging conditions for super-resolution fluorescence microscopy and EM are often not compatible. Here, we have developed several experiment protocols for correlative stochastic optical reconstruction microscopy (STORM) and EM methods, both for un-embedded samples by applying EM-specific sample preparations after STORM imaging and for embedded and sectioned samples by optimizing the fluorescence under EM fixation, staining and embedding conditions. We demonstrated these methods using a variety of cellular targets. PMID:25874453

Nano-scale measurement of biomolecules by optical microscopy and semiconductor nanoparticles

PubMed Central

Ichimura, Taro; Jin, Takashi; Fujita, Hideaki; Higuchi, Hideo; Watanabe, Tomonobu M.

2014-01-01

Over the past decade, great developments in optical microscopy have made this technology increasingly compatible with biological studies. Fluorescence microscopy has especially contributed to investigating the dynamic behaviors of live specimens and can now resolve objects with nanometer precision and resolution due to super-resolution imaging. Additionally, single particle tracking provides information on the dynamics of individual proteins at the nanometer scale both in vitro and in cells. Complementing advances in microscopy technologies has been the development of fluorescent probes. The quantum dot, a semi-conductor fluorescent nanoparticle, is particularly suitable for single particle tracking and super-resolution imaging. This article overviews the principles of single particle tracking and super resolution along with describing their application to the nanometer measurement/observation of biological systems when combined with quantum dot technologies. PMID:25120488

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

NASA Astrophysics Data System (ADS)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

Super-Resolution Microscopy Techniques and Their Potential for Applications in Radiation Biophysics.

PubMed

Eberle, Jan Philipp; Rapp, Alexander; Krufczik, Matthias; Eryilmaz, Marion; Gunkel, Manuel; Erfle, Holger; Hausmann, Michael

2017-01-01

Fluorescence microscopy is an essential tool for imaging tagged biological structures. Due to the wave nature of light, the resolution of a conventional fluorescence microscope is limited laterally to about 200 nm and axially to about 600 nm, which is often referred to as the Abbe limit. This hampers the observation of important biological structures and dynamics in the nano-scaled range ~10 nm to ~100 nm. Consequentially, various methods have been developed circumventing this limit of resolution. Super-resolution microscopy comprises several of those methods employing physical and/or chemical properties, such as optical/instrumental modifications and specific labeling of samples. In this article, we will give a brief insight into a variety of selected optical microscopy methods reaching super-resolution beyond the Abbe limit. We will survey three different concepts in connection to biological applications in radiation research without making a claim to be complete.

Understanding Super-Resolution Nanoscopy and Its Biological Applications in Cell Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Dehong; Zhao, Baoming; Xie, Yumei

2013-01-01

Optical microscopy has been an ideal tool to study phenomena in live cells because visible light at reasonable intensity does not perturb much of the normal biological functions. However, optical resolution using visible light is significantly limited by the wavelength. Overcoming this diffraction-limit barrier will reveal biological mechanisms, cellular structures, and physiological processes at nanometer scale, orders of magnitude lower than current optical microscopy. Although this appears to be a daunting task, recently developed photoswitchable probes enable reconstruction of individual images into a super-resolution image, thus the emergence of nanoscopy. Harnessing the resolution power of nanoscopy, we report here nano-resolutionmore » fluorescence imaging of microtubules and their network structures in biological cells. The super-resolution nanoscopy successfully resolved nanostructures of microtubule network—a daunting task that cannot be completed using conventional wide-field microscopy.« less

Optical super-resolution effect induced by nonlinear characteristics of graphene oxide films

NASA Astrophysics Data System (ADS)

Zhao, Yong-chuang; Nie, Zhong-quan; Zhai, Ai-ping; Tian, Yan-ting; Liu, Chao; Shi, Chang-kun; Jia, Bao-hua

2018-01-01

In this work, we focus on the optical super-resolution effect induced by strong nonlinear saturation absorption (NSA) of graphene oxide (GO) membranes. The third-order optical nonlinearities are characterized by the canonical Z-scan technique under femtosecond laser (wavelength: 800 nm, pulse width: 100 fs) excitation. Through controlling the applied femtosecond laser energy, NSA of the GO films can be tuned continuously. The GO film is placed at the focal plane as a unique amplitude filter to improve the resolution of the focused field. A multi-layer system model is proposed to present the generation of a deep sub-wavelength spot associated with the nonlinearity of GO films. Moreover, the parameter conditions to achieve the best resolution (˜λ/6) are determined entirely. The demonstrated results here are useful for high density optical recoding and storage, nanolithography, and super-resolution optical imaging.

Control of discrete time systems based on recurrent Super-Twisting-like algorithm.

PubMed

Salgado, I; Kamal, S; Bandyopadhyay, B; Chairez, I; Fridman, L

2016-09-01

Most of the research in sliding mode theory has been carried out to in continuous time to solve the estimation and control problems. However, in discrete time, the results in high order sliding modes have been less developed. In this paper, a discrete time super-twisting-like algorithm (DSTA) was proposed to solve the problems of control and state estimation. The stability proof was developed in terms of the discrete time Lyapunov approach and the linear matrix inequalities theory. The system trajectories were ultimately bounded inside a small region dependent on the sampling period. Simulation results tested the DSTA. The DSTA was applied as a controller for a Furuta pendulum and for a DC motor supplied by a DSTA signal differentiator. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Super-Resolution Imaging of a Dielectric Microsphere Is Governed by the Waist of Its Photonic Nanojet.

PubMed

Yang, Hui; Trouillon, Raphaël; Huszka, Gergely; Gijs, Martin A M

2016-08-10

Dielectric microspheres with appropriate refractive index can image objects with super-resolution, that is, with a precision well beyond the classical diffraction limit. A microsphere is also known to generate upon illumination a photonic nanojet, which is a scattered beam of light with a high-intensity main lobe and very narrow waist. Here, we report a systematic study of the imaging of water-immersed nanostructures by barium titanate glass microspheres of different size. A numerical study of the light propagation through a microsphere points out the light focusing capability of microspheres of different size and the waist of their photonic nanojet. The former correlates to the magnification factor of the virtual images obtained from linear test nanostructures, the biggest magnification being obtained with microspheres of ∼6-7 μm in size. Analyzing the light intensity distribution of microscopy images allows determining analytically the point spread function of the optical system and thereby quantifies its resolution. We find that the super-resolution imaging of a microsphere is dependent on the waist of its photonic nanojet, the best resolution being obtained with a 6 μm Ø microsphere, which generates the nanojet with the minimum waist. This comparison allows elucidating the super-resolution imaging mechanism.

Three-Dimensional Localization of Single Molecules for Super-Resolution Imaging and Single-Particle Tracking

PubMed Central

von Diezmann, Alex; Shechtman, Yoav; Moerner, W. E.

2017-01-01

Single-molecule super-resolution fluorescence microscopy and single-particle tracking are two imaging modalities that illuminate the properties of cells and materials on spatial scales down to tens of nanometers, or with dynamical information about nanoscale particle motion in the millisecond range, respectively. These methods generally use wide-field microscopes and two-dimensional camera detectors to localize molecules to much higher precision than the diffraction limit. Given the limited total photons available from each single-molecule label, both modalities require careful mathematical analysis and image processing. Much more information can be obtained about the system under study by extending to three-dimensional (3D) single-molecule localization: without this capability, visualization of structures or motions extending in the axial direction can easily be missed or confused, compromising scientific understanding. A variety of methods for obtaining both 3D super-resolution images and 3D tracking information have been devised, each with their own strengths and weaknesses. These include imaging of multiple focal planes, point-spread-function engineering, and interferometric detection. These methods may be compared based on their ability to provide accurate and precise position information of single-molecule emitters with limited photons. To successfully apply and further develop these methods, it is essential to consider many practical concerns, including the effects of optical aberrations, field-dependence in the imaging system, fluorophore labeling density, and registration between different color channels. Selected examples of 3D super-resolution imaging and tracking are described for illustration from a variety of biological contexts and with a variety of methods, demonstrating the power of 3D localization for understanding complex systems. PMID:28151646

"Supertrap" at Work: Extremely Efficient Nonradiative Recombination Channels in MAPbI3 Perovskites Revealed by Luminescence Super-Resolution Imaging and Spectroscopy.

PubMed

Merdasa, Aboma; Tian, Yuxi; Camacho, Rafael; Dobrovolsky, Alexander; Debroye, Elke; Unger, Eva L; Hofkens, Johan; Sundström, Villy; Scheblykin, Ivan G

2017-06-27

Organo-metal halide perovskites are some of the most promising materials for the new generation of low-cost photovoltaic and light-emitting devices. Their solution processability is a beneficial trait, although it leads to a spatial inhomogeneity of perovskite films with a variation of the trap state density at the nanoscale. Comprehending their properties using traditional spectroscopy therefore becomes difficult, calling for a combination with microscopy in order to see beyond the ensemble-averaged response. We studied photoluminescence (PL) blinking of micrometer-sized individual methylammonium lead iodide (MAPbI 3 ) perovskite polycrystals, as well as monocrystalline microrods up to 10 μm long. We correlated their PL dynamics with structure employing scanning electron and optical super-resolution microscopy. Combining super-resolution localization imaging and super-resolution optical fluctuation imaging (SOFI), we could detect and quantify preferential emitting regions in polycrystals exhibiting different types of blinking. We propose that blinking in MAPbI 3 occurs by the activation/passivation of a "supertrap" which presumably is a donor-acceptor pair able to trap both electrons and holes. As such, nonradiative recombination via supertraps, in spite being present at a rather low concentrations (10 12 -10 15 cm -3 ), is much more efficient than via all other defect states present in the material at higher concentrations (10 16 -10 18 cm -3 ). We speculate that activation/deactivation of a supertrap occurs by its temporary dissociation into free donor and acceptor impurities. We found that supertraps are most efficient in structurally homogeneous and large MAPbI 3 crystals where carrier diffusion is efficient, which may therefore pose limitations on the efficiency of perovskite-based devices.

Modeling super-resolution SERS using a T-matrix method to elucidate molecule-nanoparticle coupling and the origins of localization errors

NASA Astrophysics Data System (ADS)

Heaps, Charles W.; Schatz, George C.

2017-06-01

A computational method to model diffraction-limited images from super-resolution surface-enhanced Raman scattering microscopy is introduced. Despite significant experimental progress in plasmon-based super-resolution imaging, theoretical predictions of the diffraction limited images remain a challenge. The method is used to calculate localization errors and image intensities for a single spherical gold nanoparticle-molecule system. The light scattering is calculated using a modification of generalized Mie (T-matrix) theory with a point dipole source and diffraction limited images are calculated using vectorial diffraction theory. The calculation produces the multipole expansion for each emitter and the coherent superposition of all fields. Imaging the constituent fields in addition to the total field provides new insight into the strong coupling between the molecule and the nanoparticle. Regardless of whether the molecular dipole moment is oriented parallel or perpendicular to the nanoparticle surface, the anisotropic excitation distorts the center of the nanoparticle as measured by the point spread function by approximately fifty percent of the particle radius toward to the molecule. Inspection of the nanoparticle multipoles reveals that distortion arises from a weak quadrupole resonance interfering with the dipole field in the nanoparticle. When the nanoparticle-molecule fields are in-phase, the distorted nanoparticle field dominates the observed image. When out-of-phase, the nanoparticle and molecule are of comparable intensity and interference between the two emitters dominates the observed image. The method is also applied to different wavelengths and particle radii. At off-resonant wavelengths, the method predicts images closer to the molecule not because of relative intensities but because of greater distortion in the nanoparticle. The method is a promising approach to improving the understanding of plasmon-enhanced super-resolution experiments.

Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle camera

PubMed Central

Israel, Yonatan; Tenne, Ron; Oron, Dan; Silberberg, Yaron

2017-01-01

Despite advances in low-light-level detection, single-photon methods such as photon correlation have rarely been used in the context of imaging. The few demonstrations, for example of subdiffraction-limited imaging utilizing quantum statistics of photons, have remained in the realm of proof-of-principle demonstrations. This is primarily due to a combination of low values of fill factors, quantum efficiencies, frame rates and signal-to-noise characteristic of most available single-photon sensitive imaging detectors. Here we describe an imaging device based on a fibre bundle coupled to single-photon avalanche detectors that combines a large fill factor, a high quantum efficiency, a low noise and scalable architecture. Our device enables localization-based super-resolution microscopy in a non-sparse non-stationary scene, utilizing information on the number of active emitters, as gathered from non-classical photon statistics. PMID:28287167

Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2016-05-01

We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance the face resolution for improved human face recognition performance.

Joint estimation of high resolution images and depth maps from light field cameras

NASA Astrophysics Data System (ADS)

Ohashi, Kazuki; Takahashi, Keita; Fujii, Toshiaki

2014-03-01

Light field cameras are attracting much attention as tools for acquiring 3D information of a scene through a single camera. The main drawback of typical lenselet-based light field cameras is the limited resolution. This limitation comes from the structure where a microlens array is inserted between the sensor and the main lens. The microlens array projects 4D light field on a single 2D image sensor at the sacrifice of the resolution; the angular resolution and the position resolution trade-off under the fixed resolution of the image sensor. This fundamental trade-off remains after the raw light field image is converted to a set of sub-aperture images. The purpose of our study is to estimate a higher resolution image from low resolution sub-aperture images using a framework of super-resolution reconstruction. In this reconstruction, these sub-aperture images should be registered as accurately as possible. This registration is equivalent to depth estimation. Therefore, we propose a method where super-resolution and depth refinement are performed alternatively. Most of the process of our method is implemented by image processing operations. We present several experimental results using a Lytro camera, where we increased the resolution of a sub-aperture image by three times horizontally and vertically. Our method can produce clearer images compared to the original sub-aperture images and the case without depth refinement.

Barnacle Bill in Super Resolution from Super Panorama

NASA Image and Video Library

1998-07-03

"Barnacle Bill" is a small rock immediately west-northwest of the Mars Pathfinder lander and was the first rock visited by the Sojourner Rover's alpha proton X-ray spectrometer (APXS) instrument. This image shows super resolution techniques applied to the first APXS target rock, which was never imaged with the rover's forward cameras. Super resolution was applied to help to address questions about the texture of this rock and what it might tell us about its mode of origin. This view of Barnacle Bill was produced by combining the "Super Panorama" frames from the IMP camera. Super resolution was applied to help to address questions about the texture of these rocks and what it might tell us about their mode of origin. The composite color frames that make up this anaglyph were produced for both the right and left eye of the IMP. The composites consist of 7 frames in the right eye and 8 frames in the left eye, taken with different color filters that were enlarged by 500% and then co-added using Adobe Photoshop to produce, in effect, a super-resolution panchromatic frame that is sharper than an individual frame would be. These panchromatic frames were then colorized with the red, green, and blue filtered images from the same sequence. The color balance was adjusted to approximate the true color of Mars. The anaglyph view was produced by combining the left with the right eye color composite frames by assigning the left eye composite view to the red color plane and the right eye composite view to the green and blue color planes (cyan), to produce a stereo anaglyph mosaic. This mosaic can be viewed in 3-D on your computer monitor or in color print form by wearing red-blue 3-D glasses. http://photojournal.jpl.nasa.gov/catalog/PIA01409

Differential localization of SAP102 and PSD-95 is revealed in hippocampal spines using super-resolution light microscopy.

PubMed

Zheng, Chan-Ying; Wang, Ya-Xia; Kachar, Bechara; Petralia, Ronald S

2011-01-01

Synapse-associated protein 102 (SAP102) and postsynaptic density 95 (PSD-95) are two major cytoskeleton proteins in the postsynaptic density (PSD). Both of them belong to the membrane-associated guanylate kinase (MAGUK) family, which clusters and anchors glutamate receptors and other proteins at synapses. In our previous study, we found that SAP102 and PSD-95 have different distributions, using combined light/electron microscopy (LM/EM) methods.1 Here, we double labeled endogenous SAP102 and PSD-95 in mature hippocampal neurons, and then took images by two different kinds of super resolution microscopy-Stimulated Emission Depletion microscopy (STED) and DeltaVision OMX 3D super resolution microscopy. We found that our 2D and 3D super resolution data were consistent with our previous LM/EM data, showing significant differences in the localization of SAP102 and PSD-95 in spines: SAP102 is distributed in both the PSD and cytoplasm of spines, while PSD-95 is concentrated only in the PSD area. These results indicate functional differences between SAP102 and PSD-95 in synaptic organization and plasticity.

Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue

PubMed Central

Spühler, Isabelle A.; Conley, Gaurasundar M.; Scheffold, Frank; Sprecher, Simon G.

2016-01-01

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation. PMID:27303270

Super Resolution Imaging of Genetically Labeled Synapses in Drosophila Brain Tissue.

PubMed

Spühler, Isabelle A; Conley, Gaurasundar M; Scheffold, Frank; Sprecher, Simon G

2016-01-01

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation.

Super Resolution Algorithm for CCTVs

NASA Astrophysics Data System (ADS)

Gohshi, Seiichi

2015-03-01

Recently, security cameras and CCTV systems have become an important part of our daily lives. The rising demand for such systems has created business opportunities in this field, especially in big cities. Analogue CCTV systems are being replaced by digital systems, and HDTV CCTV has become quite common. HDTV CCTV can achieve images with high contrast and decent quality if they are clicked in daylight. However, the quality of an image clicked at night does not always have sufficient contrast and resolution because of poor lighting conditions. CCTV systems depend on infrared light at night to compensate for insufficient lighting conditions, thereby producing monochrome images and videos. However, these images and videos do not have high contrast and are blurred. We propose a nonlinear signal processing technique that significantly improves visual and image qualities (contrast and resolution) of low-contrast infrared images. The proposed method enables the use of infrared cameras for various purposes such as night shot and poor lighting environments under poor lighting conditions.

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation

NASA Astrophysics Data System (ADS)

Chi, Yuejie

2017-08-01

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

Development of plenoptic infrared camera using low dimensional material based photodetectors

NASA Astrophysics Data System (ADS)

Chen, Liangliang

Infrared (IR) sensor has extended imaging from submicron visible spectrum to tens of microns wavelength, which has been widely used for military and civilian application. The conventional bulk semiconductor materials based IR cameras suffer from low frame rate, low resolution, temperature dependent and highly cost, while the unusual Carbon Nanotube (CNT), low dimensional material based nanotechnology has been made much progress in research and industry. The unique properties of CNT lead to investigate CNT based IR photodetectors and imaging system, resolving the sensitivity, speed and cooling difficulties in state of the art IR imagings. The reliability and stability is critical to the transition from nano science to nano engineering especially for infrared sensing. It is not only for the fundamental understanding of CNT photoresponse induced processes, but also for the development of a novel infrared sensitive material with unique optical and electrical features. In the proposed research, the sandwich-structured sensor was fabricated within two polymer layers. The substrate polyimide provided sensor with isolation to background noise, and top parylene packing blocked humid environmental factors. At the same time, the fabrication process was optimized by real time electrical detection dielectrophoresis and multiple annealing to improve fabrication yield and sensor performance. The nanoscale infrared photodetector was characterized by digital microscopy and precise linear stage in order for fully understanding it. Besides, the low noise, high gain readout system was designed together with CNT photodetector to make the nano sensor IR camera available. To explore more of infrared light, we employ compressive sensing algorithm into light field sampling, 3-D camera and compressive video sensing. The redundant of whole light field, including angular images for light field, binocular images for 3-D camera and temporal information of video streams, are extracted and expressed in compressive approach. The following computational algorithms are applied to reconstruct images beyond 2D static information. The super resolution signal processing was then used to enhance and improve the image spatial resolution. The whole camera system brings a deeply detailed content for infrared spectrum sensing.

Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2014-01-01

Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes. PMID:24566632

Sparse representations-based super-resolution of key-frames extracted from frames-sequences generated by a visual sensor network.

PubMed

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2014-02-21

Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes.

Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems

NASA Technical Reports Server (NTRS)

Pagnutti, Mary

2006-01-01

This viewgraph presentation reviews the creation of a prototype algorithm for atmospheric correction using high spatial resolution earth observing imaging systems. The objective of the work was to evaluate accuracy of a prototype algorithm that uses satellite-derived atmospheric products to generate scene reflectance maps for high spatial resolution (HSR) systems. This presentation focused on preliminary results of only the satellite-based atmospheric correction algorithm.

Elemental Abundances of Ultra-Heavy Galactic Cosmic Rays from the SuperTIGER Instrument

NASA Astrophysics Data System (ADS)

Murphy, Ryan

2016-07-01

The SuperTIGER (Trans-Iron Galactic Element Recorder) experiment was launched on a long-duration balloon flight from Williams Field, Antarctica, on December 8, 2012. The instrument measured the relative elemental abundances of Galactic Cosmic Rays (GCR) for charge (Z) Z>10 with excellent charge resolution, displaying well resolved individual element peaks for 10 ≤ Z ≤ 40. During its record-breaking 55-day flight, SuperTIGER collected ˜4.73 x10^{6} Iron nuclei, ˜8 times as many as detected by its predecessor, TIGER, with charge resolution at iron of 0.17 cu. SuperTIGER measures charge (Z) and energy (E) using a combination of three scintillator and two Cherenkov detectors, and employs a scintillating fiber hodoscope for event trajectory determination. The SuperTIGER data have been analyzed to correct for instrument effects and remove events that underwent nuclear interactions within the instrument. The data include more than 600 events in the charge range 30 < Z ≤ 40. SuperTIGER is the first experiment to resolve elemental abundances of every element in this charge range with high statistics and single-element resolution. The relative abundances of the galactic cosmic ray source have been derived from the measured relative elemental abundances using atmospheric and interstellar propagations. The SuperTIGER measured abundances are generally consistent with previous experimental results from TIGER and ACE-CRIS, with improved statistical precision. The SuperTIGER results confirm the earlier results from TIGER, supporting a model of cosmic-ray origin in OB associations, with preferential acceleration of refractory elements over volatile elements ordered by atomic mass (A). A second SuperTIGER Antarctic flight is planned for December 2017. Details of the instrument, flight, data analysis, and ongoing preparations will be presented.

Stand-off explosive detection utilizing low power stimulated emission nuclear quadrupole resonance detection and subwavelength focusing wideband super lens

NASA Astrophysics Data System (ADS)

Apostolos, John; Mouyos, William; Feng, Judy; Chase, Walter

2015-05-01

The need for advanced techniques to detect improvised explosive devices (IED) at stand-off distances greater than ten (10) meters has driven AMI Research and Development (AMI) to develop a solution to detect and identify the threat utilizing a forward looking Synthetic Aperture Radar (SAR) combined with our CW radar technology Nuclear Quadrupole Resonance (NQR) detection system. The novel features include a near-field sub-wavelength focusing antenna, a wide band 300 KHz to 300 MHz rapidly scanning CW radar facilitated by a high Q antenna/tuner, and an advanced processor utilizing Rabi transitions where the nucleus oscillates between states under the time dependent incident electromagnetic field and alternately absorbs energy from the incident field while emitting coherent energy via stimulated emission. AMI's Sub-wavelength Focusing Wide Band Super Lens uses a Near-Field SAR, making detection possible at distances greater than ten (10) meters. This super lens is capable of operating on the near-field and focusing electromagnetic waves to resolutions beyond the diffraction limit. When applied to the case of a vehicle approaching an explosive hazard the methodologies of synthetic aperture radar is fused with the array based super resolution and the NQR data processing detecting the explosive hazard.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

1D-VAR Retrieval Using Superchannels

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel; Larar, Allen; Smith, William L.; Schluessel, Peter; Mango, Stephen; SaintGermain, Karen

2008-01-01

Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs.

PubMed

Zheng, Yu; Yang, Yang; Chen, Wu

2017-06-25

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm.

State-Based Implicit Coordination and Applications

NASA Technical Reports Server (NTRS)

Narkawicz, Anthony J.; Munoz, Cesar A.

2011-01-01

In air traffic management, pairwise coordination is the ability to achieve separation requirements when conflicting aircraft simultaneously maneuver to solve a conflict. Resolution algorithms are implicitly coordinated if they provide coordinated resolution maneuvers to conflicting aircraft when only surveillance data, e.g., position and velocity vectors, is periodically broadcast by the aircraft. This paper proposes an abstract framework for reasoning about state-based implicit coordination. The framework consists of a formalized mathematical development that enables and simplifies the design and verification of implicitly coordinated state-based resolution algorithms. The use of the framework is illustrated with several examples of algorithms and formal proofs of their coordination properties. The work presented here supports the safety case for a distributed self-separation air traffic management concept where different aircraft may use different conflict resolution algorithms and be assured that separation will be maintained.

Passive Super-Low Frequency electromagnetic prospecting technique

NASA Astrophysics Data System (ADS)

Wang, Nan; Zhao, Shanshan; Hui, Jian; Qin, Qiming

2017-03-01

The Super-Low Frequency (SLF) electromagnetic prospecting technique, adopted as a non-imaging remote sensing tool for depth sounding, is systematically proposed for subsurface geological survey. In this paper, we propose and theoretically illustrate natural source magnetic amplitudes as SLF responses for the first step. In order to directly calculate multi-dimensional theoretical SLF responses, modeling algorithms were developed and evaluated using the finite difference method. The theoretical results of three-dimensional (3-D) models show that the average normalized SLF magnetic amplitude responses were numerically stable and appropriate for practical interpretation. To explore the depth resolution, three-layer models were configured. The modeling results prove that the SLF technique is more sensitive to conductive objective layers than high resistive ones, with the SLF responses of conductive objective layers obviously showing uprising amplitudes in the low frequency range. Afterwards, we proposed an improved Frequency-Depth transformation based on Bostick inversion to realize the depth sounding by empirically adjusting two parameters. The SLF technique has already been successfully applied in geothermal exploration and coalbed methane (CBM) reservoir interpretation, which demonstrates that the proposed methodology is effective in revealing low resistive distributions. Furthermore, it siginificantly contributes to reservoir identification with electromagnetic radiation anomaly extraction. Meanwhile, the SLF interpretation results are in accordance with dynamic production status of CBM reservoirs, which means it could provide an economical, convenient and promising method for exploring and monitoring subsurface geo-objects.

TH-CD-206-05: Machine-Learning Based Segmentation of Organs at Risks for Head and Neck Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibragimov, B; Pernus, F; Strojan, P

Purpose: Accurate and efficient delineation of tumor target and organs-at-risks is essential for the success of radiotherapy. In reality, despite of decades of intense research efforts, auto-segmentation has not yet become clinical practice. In this study, we present, for the first time, a deep learning-based classification algorithm for autonomous segmentation in head and neck (HaN) treatment planning. Methods: Fifteen HN datasets of CT, MR and PET images with manual annotation of organs-at-risk (OARs) including spinal cord, brainstem, optic nerves, chiasm, eyes, mandible, tongue, parotid glands were collected and saved in a library of plans. We also have ten super-resolution MRmore » images of the tongue area, where the genioglossus and inferior longitudinalis tongue muscles are defined as organs of interest. We applied the concepts of random forest- and deep learning-based object classification for automated image annotation with the aim of using machine learning to facilitate head and neck radiotherapy planning process. In this new paradigm of segmentation, random forests were used for landmark-assisted segmentation of super-resolution MR images. Alternatively to auto-segmentation with random forest-based landmark detection, deep convolutional neural networks were developed for voxel-wise segmentation of OARs in single and multi-modal images. The network consisted of three pairs of convolution and pooing layer, one RuLU layer and a softmax layer. Results: We present a comprehensive study on using machine learning concepts for auto-segmentation of OARs and tongue muscles for the HaN radiotherapy planning. An accuracy of 81.8% in terms of Dice coefficient was achieved for segmentation of genioglossus and inferior longitudinalis tongue muscles. Preliminary results of OARs regimentation also indicate that deep-learning afforded an unprecedented opportunities to improve the accuracy and robustness of radiotherapy planning. Conclusion: A novel machine learning framework has been developed for image annotation and structure segmentation. Our results indicate the great potential of deep learning in radiotherapy treatment planning.« less

Fast dictionary generation and searching for magnetic resonance fingerprinting.

PubMed

Jun Xie; Mengye Lyu; Jian Zhang; Hui, Edward S; Wu, Ed X; Ze Wang

2017-07-01

A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.

Passive Standoff Super Resolution Imaging using Spatial-Spectral Multiplexing

DTIC Science & Technology

2017-08-14

94 5.0 Four -Dimensional Object-Space Data Reconstruction Using Spatial...103 5.3 Four -dimensional scene reconstruction using SSM...transitioning to systems based on spectrally resolved longitudinal spatial coherence interferometry. This document also includes research related to four

Lateral resolution improvement in scanning nonlinear dielectric microscopy by measuring super-higher-order nonlinear dielectric constants

NASA Astrophysics Data System (ADS)

Chinone, N.; Yamasue, K.; Hiranaga, Y.; Honda, K.; Cho, Y.

2012-11-01

Scanning nonlinear dielectric microscopy (SNDM) can be used to visualize polarization distributions in ferroelectric materials and dopant profiles in semiconductor devices. Without using a special sharp tip, we achieved an improved lateral resolution in SNDM through the measurement of super-higher-order nonlinearity up to the fourth order. We observed a multidomain single crystal congruent LiTaO3 (CLT) sample, and a cross section of a metal-oxide-semiconductor (MOS) field-effect-transistor (FET). The imaged domain boundaries of the CLT were narrower in the super-higher-order images than in the conventional image. Compared to the conventional method, the super-higher-order method resolved the more detailed structure of the MOSFET.

The internal architecture of dendritic spines revealed by super-resolution imaging: What did we learn so far?

DOE Office of Scientific and Technical Information (OSTI.GOV)

MacGillavry, Harold D., E-mail: h.d.macgillavry@uu.nl; Hoogenraad, Casper C., E-mail: c.hoogenraad@uu.nl

2015-07-15

The molecular architecture of dendritic spines defines the efficiency of signal transmission across excitatory synapses. It is therefore critical to understand the mechanisms that control the dynamic localization of the molecular constituents within spines. However, because of the small scale at which most processes within spines take place, conventional light microscopy techniques are not adequate to provide the necessary level of resolution. Recently, super-resolution imaging techniques have overcome the classical barrier imposed by the diffraction of light, and can now resolve the localization and dynamic behavior of proteins within small compartments with nanometer precision, revolutionizing the study of dendritic spinemore » architecture. Here, we highlight exciting new findings from recent super-resolution studies on neuronal spines, and discuss how these studies revealed important new insights into how protein complexes are assembled and how their dynamic behavior shapes the efficiency of synaptic transmission.« less

Perspectives in Super-resolved Fluorescence Microscopy: What comes next?

NASA Astrophysics Data System (ADS)

Cremer, Christoph; Birk, Udo

2016-04-01

The Nobel Prize in Chemistry 2014 has been awarded to three scientists involved in the development of STED and PALM super-resolution fluorescence microscopy (SRM) methods. They have proven that it is possible to overcome the hundred year old theoretical limit for the resolution potential of light microscopy (of about 200 nm for visible light), which for decades has precluded a direct glimpse of the molecular machinery of life. None of the present-day super-resolution techniques have invalidated the Abbe limit for light optical detection; however, they have found clever ways around it. In this report, we discuss some of the challenges still to be resolved before arising SRM approaches will be fit to bring about the revolution in Biology and Medicine envisaged. Some of the challenges discussed are the applicability to image live and/or large samples, the further enhancement of resolution, future developments of labels, and multi-spectral approaches.

Super-resolution photon-efficient imaging by nanometric double-helix point spread function localization of emitters (SPINDLE)

PubMed Central

Grover, Ginni; DeLuca, Keith; Quirin, Sean; DeLuca, Jennifer; Piestun, Rafael

2012-01-01

Super-resolution imaging with photo-activatable or photo-switchable probes is a promising tool in biological applications to reveal previously unresolved intra-cellular details with visible light. This field benefits from developments in the areas of molecular probes, optical systems, and computational post-processing of the data. The joint design of optics and reconstruction processes using double-helix point spread functions (DH-PSF) provides high resolution three-dimensional (3D) imaging over a long depth-of-field. We demonstrate for the first time a method integrating a Fisher information efficient DH-PSF design, a surface relief optical phase mask, and an optimal 3D localization estimator. 3D super-resolution imaging using photo-switchable dyes reveals the 3D microtubule network in mammalian cells with localization precision approaching the information theoretical limit over a depth of 1.2 µm. PMID:23187521

Discovery of Super-Thin Disks in Nearby Edge-on Spiral Galaxies

NASA Astrophysics Data System (ADS)

Schechtman-Rook, A.; Bershady, M. A.

2014-03-01

We report the identification of a super-thin disk (hz˜ 60 pc) in the edge-on spiral galaxy NGC 891. This component is only apparent after we perform a physically motivated attenuation correction, based on detailed radiation transfer models, to our sub-arcsecond resolution near-infrared imaging. In addition to the super-thin disk, we also find several structural features near the center of NGC 891, including an inner disk truncation at ˜3 kpc. Inner disk truncations may be commonplace among massive spiral galaxies, possibly due to the effects of instabilities, such as bars. Having successfully demonstrated our methods, we are poised to apply them to a small sample of nearby edge-on galaxies, consisting both of massive and low-mass spirals.

Super-achromatic microprobe for ultrahigh-resolution endoscopic OCT imaging at 800 nm (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yuan, Wu; Alemohammad, Milad; Yu, Xiaoyun; Yu, Shaoyong; Li, Xingde

2016-03-01

In this paper, we report a super-achromatic microprobe made with fiber-optic ball lens to enable ultrahigh-resolution endoscopic OCT imaging. An axial resolution of ~2.4 µm (in air) can be achieved with a 7-fs Ti:Sapphire laser. The microprobe has minimal astigmatism which affords a high transverse resolution of ~5.6 µm. The miniaturized microprobe has an outer diameter of ~520 µm including the encasing metal guard and can be used to image small luminal organs. The performance of the ultrahigh-resolution OCT microprobe was demonstrated by imaging rat esophagus, guinea pig esophagus, and mouse rectum in vivo.

SIL-STED microscopy technique enhancing super-resolution of fluorescence microscopy

NASA Astrophysics Data System (ADS)

Park, No-Cheol; Lim, Geon; Lee, Won-sup; Moon, Hyungbae; Choi, Guk-Jong; Park, Young-Pil

2017-08-01

We have characterized a new type STED microscope which combines a high numerical aperture (NA) optical head with a solid immersion lens (SIL), and we call it as SIL-STED microscope. The advantage of a SIL-STED microscope is that its high NA of the SIL makes it superior to a general STED microscope in lateral resolution, thus overcoming the optical diffraction limit at the macromolecular level and enabling advanced super-resolution imaging of cell surface or cell membrane structure and function Do. This study presents the first implementation of higher NA illumination in a STED microscope limiting the fluorescence lateral resolution to about 40 nm. The refractive index of the SIL which is made of material KTaO3 is about 2.23 and 2.20 at a wavelength of 633 nm and 780 nm which are used for excitation and depletion in STED imaging, respectively. Based on the vector diffraction theory, the electric field focused by the SILSTED microscope is numerically calculated so that the numerical results of the point dispersion function of the microscope and the expected resolution could be analyzed. For further investigation, fluorescence imaging of nano size fluorescent beads is fulfilled to show improved performance of the technique.

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

PubMed Central

Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-01-01

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods. PMID:29652838

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

PubMed

Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-04-13

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Sparse representation based image interpolation with nonlocal autoregressive modeling.

PubMed

Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

2013-04-01

Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

VizieR Online Data Catalog: The Super-CLASS GMRT catalogue - SCG (Riseley+, 2016)

NASA Astrophysics Data System (ADS)

Riseley, C. J.; Scaife, A. M. M.; Hales, C. A.; Harrison, I.; Birkinshaw, M.; Battye, R. A.; Beswick, R. J.; Brown, M. L.; Casey, C. M.; Chapman, S. C.; Demetroullas, C.; Hung, C.-L.; Jackson, N. J.; Muxlow, T.; Watson, B.

2016-06-01

The Super-CLASS GMRT (SCG) catalogue is the low-frequency counterpart of the Super-Cluster Assisted Shear Survey. It is a survey at 13-arcsec resolution, with a limiting 5σ flux density of 170uJy. The catalogue comprises 3257 sources. (1 data file).

DURIP: Super-Resolution Module for Confocal Microscopy of Reconfigurable Matter

DTIC Science & Technology

2014-09-28

Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 superresolution microscopy, colloidal particles, self-assembly REPORT...previously have been resolved by optical microscopy. Results of Super Resolution Technique Evaluation Commercially available superresolution imaging...Weaknesses of the method are that is fundamentally a measurement that can only be deployed for fixed samples. Because superresolution is obtained by

Sparse spikes super-resolution on thin grids II: the continuous basis pursuit

NASA Astrophysics Data System (ADS)

Duval, Vincent; Peyré, Gabriel

2017-09-01

This article analyzes the performance of the continuous basis pursuit (C-BP) method for sparse super-resolution. The C-BP has been recently proposed by Ekanadham, Tranchina and Simoncelli as a refined discretization scheme for the recovery of spikes in inverse problems regularization. One of the most well known discretization scheme, the basis pursuit (BP, also known as \

Large-area super-resolution optical imaging by using core-shell microfibers

NASA Astrophysics Data System (ADS)

Liu, Cheng-Yang; Lo, Wei-Chieh

2017-09-01

We first numerically and experimentally report large-area super-resolution optical imaging achieved by using core-shell microfibers. The particular spatial electromagnetic waves for different core-shell microfibers are studied by using finite-difference time-domain and ray tracing calculations. The focusing properties of photonic nanojets are evaluated in terms of intensity profile and full width at half-maximum along propagation and transversal directions. In experiment, the general optical fiber is chemically etched down to 6 μm diameter and coated with different metallic thin films by using glancing angle deposition. The direct imaging of photonic nanojets for different core-shell microfibers is performed with a scanning optical microscope system. We show that the intensity distribution of a photonic nanojet is highly related to the metallic shell due to the surface plasmon polaritons. Furthermore, large-area super-resolution optical imaging is performed by using different core-shell microfibers placed over the nano-scale grating with 150 nm line width. The core-shell microfiber-assisted imaging is achieved with super-resolution and hundreds of times the field-of-view in contrast to microspheres. The possible applications of these core-shell optical microfibers include real-time large-area micro-fluidics and nano-structure inspections.

Recent advances in the field of super resolved imaging and sensing

NASA Astrophysics Data System (ADS)

Zalevsky, Zeev; Borkowski, Amikam; Marom, Emanuel; Javidi, Bahram; Beiderman, Yevgeny; Micó, Vicente; García, Javier

2011-05-01

In this paper we start by presenting one recent development in the field of geometric super resolution. The new approach overcomes the reduction of resolution caused by the non ideal sampling of the image done by the spatial averaging of each pixel of the sampling array. Right after, we demonstrate a remote super sensing technique allowing monitoring, from a distance, the heart beats, blood pulse pressure and the glucose level in the blood stream of a patient by tracking the trajectory of secondary speckle patterns reflected from the skin of the wrist or from the sclera.

Super-resolution pupil filtering for visual performance enhancement using adaptive optics

NASA Astrophysics Data System (ADS)

Zhao, Lina; Dai, Yun; Zhao, Junlei; Zhou, Xiaojun

2018-05-01

Ocular aberration correction can significantly improve visual function of the human eye. However, even under ideal aberration correction conditions, pupil diffraction restricts the resolution of retinal images. Pupil filtering is a simple super-resolution (SR) method that can overcome this diffraction barrier. In this study, a 145-element piezoelectric deformable mirror was used as a pupil phase filter because of its programmability and high fitting accuracy. Continuous phase-only filters were designed based on Zernike polynomial series and fitted through closed-loop adaptive optics. SR results were validated using double-pass point spread function images. Contrast sensitivity was further assessed to verify the SR effect on visual function. An F-test was conducted for nested models to statistically compare different CSFs. These results indicated CSFs for the proposed SR filter were significantly higher than the diffraction correction (p < 0.05). As such, the proposed filter design could provide useful guidance for supernormal vision optical correction of the human eye.

LAI inversion algorithm based on directional reflectance kernels.

PubMed

Tang, S; Chen, J M; Zhu, Q; Li, X; Chen, M; Sun, R; Zhou, Y; Deng, F; Xie, D

2007-11-01

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.

Implementation of Super-Encryption with Trithemius Algorithm and Double Transposition Cipher in Securing PDF Files on Android Platform

NASA Astrophysics Data System (ADS)

Budiman, M. A.; Rachmawati, D.; Jessica

2018-03-01

This study aims to combine the trithemus algorithm and double transposition cipher in file security that will be implemented to be an Android-based application. The parameters being examined are the real running time, and the complexity value. The type of file to be used is a file in PDF format. The overall result shows that the complexity of the two algorithms with duper encryption method is reported as Θ (n 2). However, the processing time required in the encryption process uses the Trithemius algorithm much faster than using the Double Transposition Cipher. With the length of plaintext and password linearly proportional to the processing time.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs

PubMed Central

Zheng, Yu; Yang, Yang; Chen, Wu

2017-01-01

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm. PMID:28672830

Label-free super-resolution with coherent nonlinear structured-illumination microscopy

NASA Astrophysics Data System (ADS)

Huttunen, Mikko J.; Abbas, Aazad; Upham, Jeremy; Boyd, Robert W.

2017-08-01

Structured-illumination microscopy enables up to a two-fold lateral resolution improvement by spatially modulating the intensity profile of the illumination beam. We propose a novel way to generalize the concept of structured illumination to nonlinear widefield modalities by spatially modulating, instead of field intensities, the phase of the incident field while interferometrically measuring the complex-valued scattered field. We numerically demonstrate that for second-order and third-order processes an almost four- and six-fold increase in lateral resolution is achievable, respectively. This procedure overcomes the conventional Abbe diffraction limit and provides new possibilities for label-free super-resolution microscopy.

High resolution laboratory grating-based x-ray phase-contrast CT

NASA Astrophysics Data System (ADS)

Viermetz, Manuel P.; Birnbacher, Lorenz J. B.; Fehringer, Andreas; Willner, Marian; Noel, Peter B.; Pfeiffer, Franz; Herzen, Julia

2017-03-01

Grating-based phase-contrast computed tomography (gbPC-CT) is a promising imaging method for imaging of soft tissue contrast without the need of any contrast agent. The focus of this study is the increase in spatial resolution without loss in sensitivity to allow visualization of pathologies comparable to the convincing results obtained at the synchrotron. To improve the effective pixel size a super-resolution reconstruction based on subpixel shifts involving a deconvolution of the image is applied on differential phase-contrast data. In our study we could achieve an effective pixel sizes of 28mm without any drawback in terms of sensitivity or the ability to measure quantitative data.

An Implementation of RC4+ Algorithm and Zig-zag Algorithm in a Super Encryption Scheme for Text Security

NASA Astrophysics Data System (ADS)

Budiman, M. A.; Amalia; Chayanie, N. I.

2018-03-01

Cryptography is the art and science of using mathematical methods to preserve message security. There are two types of cryptography, namely classical and modern cryptography. Nowadays, most people would rather use modern cryptography than classical cryptography because it is harder to break than the classical one. One of classical algorithm is the Zig-zag algorithm that uses the transposition technique: the original message is unreadable unless the person has the key to decrypt the message. To improve the security, the Zig-zag Cipher is combined with RC4+ Cipher which is one of the symmetric key algorithms in the form of stream cipher. The two algorithms are combined to make a super-encryption. By combining these two algorithms, the message will be harder to break by a cryptanalyst. The result showed that complexity of the combined algorithm is θ(n2 ), while the complexity of Zig-zag Cipher and RC4+ Cipher are θ(n2 ) and θ(n), respectively.

Super-Nyquist shaping and processing technologies for high-spectral-efficiency optical systems

NASA Astrophysics Data System (ADS)

Jia, Zhensheng; Chien, Hung-Chang; Zhang, Junwen; Dong, Ze; Cai, Yi; Yu, Jianjun

2013-12-01

The implementations of super-Nyquist pulse generation, both in a digital field using a digital-to-analog converter (DAC) or an optical filter at transmitter side, are introduced. Three corresponding signal processing algorithms at receiver are presented and compared for high spectral-efficiency (SE) optical systems employing the spectral prefiltering. Those algorithms are designed for the mitigation towards inter-symbol-interference (ISI) and inter-channel-interference (ICI) impairments by the bandwidth constraint, including 1-tap constant modulus algorithm (CMA) and 3-tap maximum likelihood sequence estimation (MLSE), regular CMA and digital filter with 2-tap MLSE, and constant multi-modulus algorithm (CMMA) with 2-tap MLSE. The principles and prefiltering tolerance are given through numerical and experimental results.

Burst wait time simulation of CALIBAN reactor at delayed super-critical state

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humbert, P.; Authier, N.; Richard, B.

2012-07-01

In the past, the super prompt critical wait time probability distribution was measured on CALIBAN fast burst reactor [4]. Afterwards, these experiments were simulated with a very good agreement by solving the non-extinction probability equation [5]. Recently, the burst wait time probability distribution has been measured at CEA-Valduc on CALIBAN at different delayed super-critical states [6]. However, in the delayed super-critical case the non-extinction probability does not give access to the wait time distribution. In this case it is necessary to compute the time dependent evolution of the full neutron count number probability distribution. In this paper we present themore » point model deterministic method used to calculate the probability distribution of the wait time before a prescribed count level taking into account prompt neutrons and delayed neutron precursors. This method is based on the solution of the time dependent adjoint Kolmogorov master equations for the number of detections using the generating function methodology [8,9,10] and inverse discrete Fourier transforms. The obtained results are then compared to the measurements and Monte-Carlo calculations based on the algorithm presented in [7]. (authors)« less

a Super Voxel-Based Riemannian Graph for Multi Scale Segmentation of LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Li, Minglei

2018-04-01

Automatically segmenting LiDAR points into respective independent partitions has become a topic of great importance in photogrammetry, remote sensing and computer vision. In this paper, we cast the problem of point cloud segmentation as a graph optimization problem by constructing a Riemannian graph. The scale space of the observed scene is explored by an octree-based over-segmentation with different depths. The over-segmentation produces many super voxels which restrict the structure of the scene and will be used as nodes of the graph. The Kruskal coordinates are used to compute edge weights that are proportional to the geodesic distance between nodes. Then we compute the edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths between super voxel nodes on the scene surface. The final segmentation results are generated by clustering similar super voxels and cutting off the weak edges in the graph. The performance of this method was evaluated on LiDAR point clouds for both indoor and outdoor scenes. Additionally, extensive comparisons to state of the art techniques show that our algorithm outperforms on many metrics.

Resolution enhancement using simultaneous couple illumination

NASA Astrophysics Data System (ADS)

Hussain, Anwar; Martínez Fuentes, José Luis

2016-10-01

A super-resolution technique based on structured illumination created by a liquid crystal on silicon spatial light modulator (LCOS-SLM) is presented. Single and simultaneous pairs of tilted beams are generated to illuminate a target object. Resolution enhancement of an optical 4f system is demonstrated by using numerical simulations. The resulting intensity images are recorded at a charged couple device (CCD) and stored in the computer memory for further processing. One dimension enhancement can be performed with only 15 images. Two dimensional complete improvement requires 153 different images. The resolution of the optical system is extended three times compared to the band limited system.

Special issue on high-resolution optical imaging

NASA Astrophysics Data System (ADS)

Smith, Peter J. S.; Davis, Ilan; Galbraith, Catherine G.; Stemmer, Andreas

2013-09-01

The pace of development in the field of advanced microscopy is truly breath-taking, and is leading to major breakthroughs in our understanding of molecular machines and cell function. This special issue of Journal of Optics draws attention to a number of interesting approaches, ranging from fluorescence and imaging of unlabelled cells, to computational methods, all of which are describing the ever increasing detail of the dynamic behaviour of molecules in the living cell. This is a field which traditionally, and currently, demonstrates a marvellous interplay between the disciplines of physics, chemistry and biology, where apparent boundaries to resolution dissolve and living cells are viewed in ever more clarity. It is fertile ground for those interested in optics and non-conventional imaging to contribute high-impact outputs in the fields of cell biology and biomedicine. The series of articles presented here has been selected to demonstrate this interdisciplinarity and to encourage all those with a background in the physical sciences to 'dip their toes' into the exciting and dynamic discoveries surrounding cell function. Although single molecule super-resolution microscopy is commercially available, specimen preparation and interpretation of single molecule data remain a major challenge for scientists wanting to adopt the techniques. The paper by Allen and Davidson [1] provides a much needed detailed introduction to the practical aspects of stochastic optical reconstruction microscopy, including sample preparation, image acquisition and image analysis, as well as a brief description of the different variants of single molecule localization microscopy. Since super-resolution microscopy is no longer restricted to three-dimensional imaging of fixed samples, the review by Fiolka [2] is a timely introduction to techniques that have been successfully applied to four-dimensional live cell super-resolution microscopy. The combination of multiple high-resolution techniques, such as the combination of light sheet and structured illumination microscopy (SIM), which efficiently utilize photon budget and avoid illuminating regions of the specimen not currently being imaged, hold the greatest promise for future biological applications. Therefore, the combined setup for SIM and single molecule localization microscopy (SMLM) described by Rossberger et al [3] will be very helpful and stimulating to advanced microscopists in further modifying their setups. The SIM image helps in identifying artefacts in SMLM reconstruction, e.g. when two active fluorophores are close together and get rejected as 'out-of-focus'. This combined setup is another way to facilitate imaging live samples. The article by Thomas et al [4] presents another advance for biological super-resolution imaging with a new approach to reconstruct optically sectioned images using structured illumination. The method produces images with higher spatial resolution and greater signal to noise compared to existing approaches. This algorithm demonstrates great promise for reconstructing biological images where the signal intensities are inherently lower. Shevchuk et al [5] present a non-optic near field approach to imaging with a review of scanning ion-conductance microscopy. This is a powerful alternative approach for examining the surface dynamics of living cells including exo and endocytosis, unlabelled, and at the level of the single event. Here they present the first data on combining this approach with fluorescence confocal microscopy—adding that extra dimension. Different approaches to label-free live cell imaging are presented in the papers by Patel et al [6], Mehta and Oldenbourg [7], as well as Rogers and Zheludev [8]. All three papers bring home the excitement of looking at live cell dynamics without reporters—Patel et al [6] review both the potential of coherent anti-Stokes Raman scattering and biological applications, where specific biomolecules are detected on the basis of their biophysical properties. Polarized light microscopy as presented by Mehta and Oldenbourg [7], describe a novel implementation of this technology to detect dichroism, and demonstrate beautifully its use in imaging unlabelled microtubules, mitochondria and lipid droplets. Sub-wavelength light focusing provides another avenue to super-resolution, and this is presented by Rogers and Zheludev [8]. Speculating on further improvements, these authors expect a resolution of 0.15λ. To date, the method has not been applied to low contrast, squishy and motile biotargets, but is included here for the clear potential to drive label-free imaging in new directions. A similar logic lies behind the inclusion of Parsons et al [9] where ultraviolet coherent diffractive imaging is further developed. These authors have demonstrated a shrink-wrap technique which reduces the integration time by a factor of 5, bringing closer the time when we have lab based imaging systems based on extreme ultraviolet and soft x-ray sources using sophisticated phase retrieval algorithms. Real biological specimens have spatially varying refractive indices that inevitably lead to aberrations and image distortions. Global refractive index matching of the embedding medium has been an historic solution, but unfortunately is not practical for live cell imaging. Adaptive optics appears an attractive solution and Simmonds and Booth [10] demonstrate the theoretical benefits of applying several adaptive optical elements, placed in different conjugate planes, to create a kind of 'inverse specimen' that unwarps phase distortions of the sample—but these have yet to be tested on real specimens. A difficulty in single molecule localization microscopy has been the determination of whether or not two molecules are colocalized. Kim et al [11] present a method for correcting bleed-through during multi-colour, single molecule localization microscopy. Such methods are welcome standards when trying to quantifiably interpret how close two molecules actually are. Rees et al [12] provide an invaluable overview of key image processing steps in localization microscopy. This paper is an excellent starting point for anyone implementing localization algorithms and the Matlab software provided will be invaluable; a strong paper on which to conclude our overview of the excellent articles brought together in this issue. One aspect brought home in several of these articles is the volume of data now being collected by high resolution live cell imaging. Data processing and image reconstruction will continue to be pressure points in the further development of instrumentation and analyses. We would hope that the series of papers presented here will motivate software engineers, optical physicists and biologists to contribute to the further development of this exciting field. References [1] Allen J R et al 2013 J. Opt. 15 094001 [2] Fiolka R et al 2013 J. Opt. 15 094002 [3] Rossberger S et al 2013 J. Opt. 15 094003 [4] Thomas B et al 2013 J. Opt. 15 094004 [5] Shevchuk A et al 2013 J. Opt. 15 094005 [6] Patel I et al 2013 J. Opt. 15 094006 [7] Mehta S B et al 2013 J. Opt. 15 094007 [8] Rogers E T F et al 2013 J. Opt. 15 094008 [9] Parsons A D et al 2013 J. Opt. 15 094009 [10] Simmonds R et al 2013 J. Opt. 15 094010 [11] Kim D et al 2013 J. Opt. 15 094011 [12] Rees E J et al 2013 J. Opt. 15 094012

Effect of probe diffusion on the SOFI imaging accuracy.

PubMed

Vandenberg, Wim; Dedecker, Peter

2017-03-23

Live-cell super-resolution fluorescence imaging is becoming commonplace for exploring biological systems, though sample dynamics can affect the imaging quality. In this work we evaluate the effect of probe diffusion on super-resolution optical fluctuation imaging (SOFI), using a theoretical model and numerical simulations based on the imaging of live cells labelled with photochromic fluorescent proteins. We find that, over a range of physiological conditions, fluorophore diffusion results in a change in the amplitude of the SOFI signal. The magnitude of this change is approximately proportional to the on-time ratio of the fluorophores. However, for photochromic fluorescent proteins this effect is unlikely to present a significant distortion in practical experiments in biological systems. Due to this lack of distortions, probe diffusion strongly enhances the SOFI imaging by avoiding spatial undersampling caused by the limited labeling density.

Structural analysis of herpes simplex virus by optical super-resolution imaging

NASA Astrophysics Data System (ADS)

Laine, Romain F.; Albecka, Anna; van de Linde, Sebastian; Rees, Eric J.; Crump, Colin M.; Kaminski, Clemens F.

2015-01-01

Herpes simplex virus type-1 (HSV-1) is one of the most widespread pathogens among humans. Although the structure of HSV-1 has been extensively investigated, the precise organization of tegument and envelope proteins remains elusive. Here we use super-resolution imaging by direct stochastic optical reconstruction microscopy (dSTORM) in combination with a model-based analysis of single-molecule localization data, to determine the position of protein layers within virus particles. We resolve different protein layers within individual HSV-1 particles using multi-colour dSTORM imaging and discriminate envelope-anchored glycoproteins from tegument proteins, both in purified virions and in virions present in infected cells. Precise characterization of HSV-1 structure was achieved by particle averaging of purified viruses and model-based analysis of the radial distribution of the tegument proteins VP16, VP1/2 and pUL37, and envelope protein gD. From this data, we propose a model of the protein organization inside the tegument.

Autofocusing Airy beam STED microscopy with long focal distance

NASA Astrophysics Data System (ADS)

Hu, Di; Liang, Yao; Chen, Yin; Chen, Zan Hui; Huang, Xu Guang

2017-12-01

Stimulated emission depletion (STED) is a very important technique in super-resolution microscopy. Until now, while autofocusing Airy beam (AAB) has been an attractive theme for both theoretical and applied researches, there are almost no report on AABs being used in STED microscopy. In this paper, we propose a novel STED microscopy based on AABs. A radially symmetric 3/2 phase plate is involved to simultaneously generate autofocusing excitation- and depletion-Airy beams. Remarkably, the AAB can auto-focus to a wavelength-scale spot with a long focal depth (several millimeters): on the contrary, the working distance of a conventional high numerical aperture (NA) objective is usually very short (about 200 μm). Our calculations indicate that the AAB based STED microscopy can achieve a super-resolution spot with FWHM of 58 nm while the focal length is 4.638 mm. Moreover, with properties of non-diffracting and self-healing, the Airy beam could enable a reduction of the scattering distortion induced by the specimens and has a great potential in imaging thick specimens.

Fast super-resolution estimation of DOA and DOD in bistatic MIMO Radar with off-grid targets

NASA Astrophysics Data System (ADS)

Zhang, Dong; Zhang, Yongshun; Zheng, Guimei; Feng, Cunqian; Tang, Jun

2018-05-01

In this paper, we focus on the problem of joint DOA and DOD estimation in Bistatic MIMO Radar using sparse reconstruction method. In traditional ways, we usually convert the 2D parameter estimation problem into 1D parameter estimation problem by Kronecker product which will enlarge the scale of the parameter estimation problem and bring more computational burden. Furthermore, it requires that the targets must fall on the predefined grids. In this paper, a 2D-off-grid model is built which can solve the grid mismatch problem of 2D parameters estimation. Then in order to solve the joint 2D sparse reconstruction problem directly and efficiently, three kinds of fast joint sparse matrix reconstruction methods are proposed which are Joint-2D-OMP algorithm, Joint-2D-SL0 algorithm and Joint-2D-SOONE algorithm. Simulation results demonstrate that our methods not only can improve the 2D parameter estimation accuracy but also reduce the computational complexity compared with the traditional Kronecker Compressed Sensing method.

Demonstration of nanoimprinted hyperlens array for high-throughput sub-diffraction imaging

NASA Astrophysics Data System (ADS)

Byun, Minsueop; Lee, Dasol; Kim, Minkyung; Kim, Yangdoo; Kim, Kwan; Ok, Jong G.; Rho, Junsuk; Lee, Heon

2017-04-01

Overcoming the resolution limit of conventional optics is regarded as the most important issue in optical imaging science and technology. Although hyperlenses, super-resolution imaging devices based on highly anisotropic dispersion relations that allow the access of high-wavevector components, have recently achieved far-field sub-diffraction imaging in real-time, the previously demonstrated devices have suffered from the extreme difficulties of both the fabrication process and the non-artificial objects placement. This results in restrictions on the practical applications of the hyperlens devices. While implementing large-scale hyperlens arrays in conventional microscopy is desirable to solve such issues, it has not been feasible to fabricate such large-scale hyperlens array with the previously used nanofabrication methods. Here, we suggest a scalable and reliable fabrication process of a large-scale hyperlens device based on direct pattern transfer techniques. We fabricate a 5 cm × 5 cm size hyperlenses array and experimentally demonstrate that it can resolve sub-diffraction features down to 160 nm under 410 nm wavelength visible light. The array-based hyperlens device will provide a simple solution for much more practical far-field and real-time super-resolution imaging which can be widely used in optics, biology, medical science, nanotechnology and other closely related interdisciplinary fields.

Detection and 3D representation of pulmonary air bubbles in HRCT volumes

NASA Astrophysics Data System (ADS)

Silva, Jose S.; Silva, Augusto F.; Santos, Beatriz S.; Madeira, Joaquim

2003-05-01

Bubble emphysema is a disease characterized by the presence of air bubbles within the lungs. With the purpose of identifying pulmonary air bubbles, two alternative methods were developed, using High Resolution Computer Tomography (HRCT) exams. The search volume is confined to the pulmonary volume through a previously developed pulmonary contour detection algorithm. The first detection method follows a slice by slice approach and uses selection criteria based on the Hounsfield levels, dimensions, shape and localization of the bubbles. Candidate regions that do not exhibit axial coherence along at least two sections are excluded. Intermediate sections are interpolated for a more realistic representation of lungs and bubbles. The second detection method, after the pulmonary volume delimitation, follows a fully 3D approach. A global threshold is applied to the entire lung volume returning candidate regions. 3D morphologic operators are used to remove spurious structures and to circumscribe the bubbles. Bubble representation is accomplished by two alternative methods. The first generates bubble surfaces based on the voxel volumes previously detected; the second method assumes that bubbles are approximately spherical. In order to obtain better 3D representations, fits super-quadrics to bubble volume. The fitting process is based on non-linear least squares optimization method, where a super-quadric is adapted to a regular grid of points defined on each bubble. All methods were applied to real and semi-synthetical data where artificial and randomly deformed bubbles were embedded in the interior of healthy lungs. Quantitative results regarding bubble geometric features are either similar to a priori known values used in simulation tests, or indicate clinically acceptable dimensions and locations when dealing with real data.

A new test set for validating predictions of protein-ligand interaction.

PubMed

Nissink, J Willem M; Murray, Chris; Hartshorn, Mike; Verdonk, Marcel L; Cole, Jason C; Taylor, Robin

2002-12-01

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk). Copyright 2002 Wiley-Liss, Inc.

Comparison of two structured illumination techniques based on different 3D illumination patterns

NASA Astrophysics Data System (ADS)

Shabani, H.; Patwary, N.; Doblas, A.; Saavedra, G.; Preza, C.

2017-02-01

Manipulating the excitation pattern in optical microscopy has led to several super-resolution techniques. Among different patterns, the lateral sinusoidal excitation was used for the first demonstration of structured illumination microscopy (SIM), which provides the fastest SIM acquisition system (based on the number of raw images required) compared to the multi-spot illumination approach. Moreover, 3D patterns that include lateral and axial variations in the illumination have attracted more attention recently as they address resolution enhancement in three dimensions. A threewave (3W) interference technique based on coherent illumination has already been shown to provide super-resolution and optical sectioning in 3D-SIM. In this paper, we investigate a novel tunable technique that creates a 3D pattern from a set of multiple incoherently illuminated parallel slits that act as light sources for a Fresnel biprism. This setup is able to modulate the illumination pattern in the object space both axially and laterally with adjustable modulation frequencies. The 3D forward model for the new system is developed here to consider the effect of the axial modulation due to the 3D patterned illumination. The performance of 3D-SIM based on 3W interference and the tunable system are investigated in simulation and compared based on two different criteria. First, restored images obtained for both 3D-SIM systems using a generalized Wiener filter are compared to determine the effect of the illumination pattern on the reconstruction. Second, the effective frequency response of both systems is studied to determine the axial and lateral resolution enhancement that is obtained in each case.

Silicon/III-V laser with super-compact diffraction grating for WDM applications in electronic-photonic integrated circuits.

PubMed

Wang, Yadong; Wei, Yongqiang; Huang, Yingyan; Tu, Yongming; Ng, Doris; Lee, Cheewei; Zheng, Yunan; Liu, Boyang; Ho, Seng-Tiong

2011-01-31

We have demonstrated a heterogeneously integrated III-V-on-Silicon laser based on an ultra-large-angle super-compact grating (SCG). The SCG enables single-wavelength operation due to its high-spectral-resolution aberration-free design, enabling wavelength division multiplexing (WDM) applications in Electronic-Photonic Integrated Circuits (EPICs). The SCG based Si/III-V laser is realized by fabricating the SCG on silicon-on-insulator (SOI) substrate. Optical gain is provided by electrically pumped heterogeneous integrated III-V material on silicon. Single-wavelength lasing at 1550 nm with an output power of over 2 mW and a lasing threshold of around 150 mA were achieved.

Super-resolution depth information from a short-wave infrared laser gated-viewing system by using correlated double sampling

NASA Astrophysics Data System (ADS)

Göhler, Benjamin; Lutzmann, Peter

2017-10-01

Primarily, a laser gated-viewing (GV) system provides range-gated 2D images without any range resolution within the range gate. By combining two GV images with slightly different gate positions, 3D information within a part of the range gate can be obtained. The depth resolution is higher (super-resolution) than the minimal gate shift step size in a tomographic sequence of the scene. For a state-of-the-art system with a typical frame rate of 20 Hz, the time difference between the two required GV images is 50 ms which may be too long in a dynamic scenario with moving objects. Therefore, we have applied this approach to the reset and signal level images of a new short-wave infrared (SWIR) GV camera whose read-out integrated circuit supports correlated double sampling (CDS) actually intended for the reduction of kTC noise (reset noise). These images are extracted from only one single laser pulse with a marginal time difference in between. The SWIR GV camera consists of 640 x 512 avalanche photodiodes based on mercury cadmium telluride with a pixel pitch of 15 μm. A Q-switched, flash lamp pumped solid-state laser with 1.57 μm wavelength (OPO), 52 mJ pulse energy after beam shaping, 7 ns pulse length and 20 Hz pulse repetition frequency is used for flash illumination. In this paper, the experimental set-up is described and the operating principle of CDS is explained. The method of deriving super-resolution depth information from a GV system by using CDS is introduced and optimized. Further, the range accuracy is estimated from measured image data.

PENTACLE: Parallelized particle-particle particle-tree code for planet formation

NASA Astrophysics Data System (ADS)

Iwasawa, Masaki; Oshino, Shoichi; Fujii, Michiko S.; Hori, Yasunori

2017-10-01

We have newly developed a parallelized particle-particle particle-tree code for planet formation, PENTACLE, which is a parallelized hybrid N-body integrator executed on a CPU-based (super)computer. PENTACLE uses a fourth-order Hermite algorithm to calculate gravitational interactions between particles within a cut-off radius and a Barnes-Hut tree method for gravity from particles beyond. It also implements an open-source library designed for full automatic parallelization of particle simulations, FDPS (Framework for Developing Particle Simulator), to parallelize a Barnes-Hut tree algorithm for a memory-distributed supercomputer. These allow us to handle 1-10 million particles in a high-resolution N-body simulation on CPU clusters for collisional dynamics, including physical collisions in a planetesimal disc. In this paper, we show the performance and the accuracy of PENTACLE in terms of \\tilde{R}_cut and a time-step Δt. It turns out that the accuracy of a hybrid N-body simulation is controlled through Δ t / \\tilde{R}_cut and Δ t / \\tilde{R}_cut ˜ 0.1 is necessary to simulate accurately the accretion process of a planet for ≥106 yr. For all those interested in large-scale particle simulations, PENTACLE, customized for planet formation, will be freely available from https://github.com/PENTACLE-Team/PENTACLE under the MIT licence.

Nanoscale Photoacoustic Tomography (nPAT) for label-free super-resolution 3D imaging of red blood cells

NASA Astrophysics Data System (ADS)

Samant, Pratik; Hernandez, Armando; Conklin, Shelby; Xiang, Liangzhong

2017-08-01

We present our results in developing nanoscale photoacoustic tomography (nPAT) for label-free super-resolution imaging in 3D. We have made progress in the development of nPAT, and have acquired our first signal. We have also performed simulations that demonstrate that nPAT is a viable imaging modality for the visualization of malaria infected red blood cells (RBCs). Our results demonstrate that nPAT is both feasible and powerful for the high resolution labelfree imaging of RBCs.

Location and Geologic Setting for the Three U.S. Mars Landers

NASA Technical Reports Server (NTRS)

Parker, T. J.; Kirk, R. L.

1999-01-01

Super resolution of the horizon at both Viking landing sites has revealed "new" features we use for triangulation, similar to the approach used during the Mars Pathfinder Mission. We propose alternative landing site locations for both landers for which we believe the confidence is very high. Super resolution of VL-1 images also reveals some of the drift material at the site to consist of gravel-size deposits. Since our proposed location for VL-2 is NOT on the Mie ejecta blanket, the blocky surface around the lander may represent the meter-scale texture of "smooth palins" in the region. The Viking Lander panchromatic images typically offer more repeat coverage than does the IMP on Mars Pathfinder, due to the longer duration of these landed missions. Sub-pixel offsets, necessary for super resolution to work, appear to be attributable to thermal effects on the lander and settling of the lander over time. Due to the greater repeat coverage (particularly in the near and mid-fields) and all-panchromatic images, the gain in resolution by super resolution processing is better for Viking than it is with most IMP image sequences. This enhances the study of textural details near the lander and enables the identification rock and surface textures at greater distances from the lander. Discernment of stereo in super resolution im-ages is possible to great distances from the lander, but is limited by the non-rotating baseline between the two cameras and the shorter height of the cameras above the ground compared to IMP. With super resolution, details of horizon features, such as blockiness and crater rim shapes, may be better correlated with Orbiter images. A number of horizon features - craters and ridges - were identified at VL-1 during the misison, and a few hils and subtle ridges were identified at VL-2. We have added a few "new" horizon features for triangulation at the VL-2 landing site in Utopia Planitia. These features were used for independent triangulation with features visible in Viking Orbiter and MGS MOC images, though the actual location of VL-1 lies in a data dropout in the MOC image of the area. Additional information is contained in the original extended abstract.

HWDA: A coherence recognition and resolution algorithm for hybrid web data aggregation

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

Aiming at the object confliction recognition and resolution problem for hybrid distributed data stream aggregation, a distributed data stream object coherence solution technology is proposed. Firstly, the framework was defined for the object coherence conflict recognition and resolution, named HWDA. Secondly, an object coherence recognition technology was proposed based on formal language description logic and hierarchical dependency relationship between logic rules. Thirdly, a conflict traversal recognition algorithm was proposed based on the defined dependency graph. Next, the conflict resolution technology was prompted based on resolution pattern matching including the definition of the three types of conflict, conflict resolution matching pattern and arbitration resolution method. At last, the experiment use two kinds of web test data sets to validate the effect of application utilizing the conflict recognition and resolution technology of HWDA.

From single-molecule spectroscopy to super-resolution imaging of the neuron: a review

PubMed Central

Laine, Romain F; Kaminski Schierle, Gabriele S; van de Linde, Sebastian; Kaminski, Clemens F

2016-01-01

Abstract For more than 20 years, single-molecule spectroscopy has been providing invaluable insights into nature at the molecular level. The field has received a powerful boost with the development of the technique into super-resolution imaging methods, ca. 10 years ago, which overcome the limitations imposed by optical diffraction. Today, single molecule super-resolution imaging is routinely used in the study of macromolecular function and structure in the cell. Concomitantly, computational methods have been developed that provide information on numbers and positions of molecules at the nanometer-scale. In this overview, we outline the technical developments that have led to the emergence of localization microscopy techniques from single-molecule spectroscopy. We then provide a comprehensive review on the application of the technique in the field of neuroscience research. PMID:28809165

Single cell systems biology by super-resolution imaging and combinatorial labeling

PubMed Central

Lubeck, Eric; Cai, Long

2012-01-01

Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral separability of fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using Fluorescence in situ Hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured the mRNA levels of 32 genes simultaneously in single S. cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells provides a natural approach to bring systems biology into single cells. PMID:22660740

Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution

PubMed Central

Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.; Edwards, Thayne L.; James, Conrad D.; Lidke, Keith A.

2016-01-01

We have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single molecule super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet. PMID:27375939

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization.

PubMed

Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui

2015-01-19

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation.

Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT

PubMed Central

dos Santos, Valentin Aranha; Schmetterer, Leopold; Triggs, Graham J.; Leitgeb, Rainer A.; Gröschl, Martin; Messner, Alina; Schmidl, Doreen; Garhofer, Gerhard; Aschinger, Gerold; Werkmeister, René M.

2016-01-01

In optical coherence tomography (OCT), the axial resolution is directly linked to the coherence length of the employed light source. It is currently unclear if OCT allows measuring thicknesses below its axial resolution value. To investigate spectral-domain OCT imaging in the super-resolution regime, we derived a signal model and compared it with the experiment. Several island thin film samples of known refractive indices and thicknesses in the range 46 – 163 nm were fabricated and imaged. Reference thickness measurements were performed using a commercial atomic force microscope. In vivo measurements of the tear film were performed in 4 healthy subjects. Our results show that quantitative super-resolved thickness measurement can be performed using OCT. In addition, we report repeatable tear film lipid layer visualization. Our results provide a novel interpretation of the OCT axial resolution limit and open a perspective to deeper extraction of the information hidden in the coherence volume. PMID:27446696

Label-free photoacoustic nanoscopy

PubMed Central

Danielli, Amos; Maslov, Konstantin; Garcia-Uribe, Alejandro; Winkler, Amy M.; Li, Chiye; Wang, Lidai; Chen, Yun; Dorn, Gerald W.; Wang, Lihong V.

2014-01-01

Abstract. Super-resolution microscopy techniques—capable of overcoming the diffraction limit of light—have opened new opportunities to explore subcellular structures and dynamics not resolvable in conventional far-field microscopy. However, relying on staining with exogenous fluorescent markers, these techniques can sometimes introduce undesired artifacts to the image, mainly due to large tagging agent sizes and insufficient or variable labeling densities. By contrast, the use of endogenous pigments allows imaging of the intrinsic structures of biological samples with unaltered molecular constituents. Here, we report label-free photoacoustic (PA) nanoscopy, which is exquisitely sensitive to optical absorption, with an 88 nm resolution. At each scanning position, multiple PA signals are successively excited with increasing laser pulse energy. Because of optical saturation or nonlinear thermal expansion, the PA amplitude depends on the nonlinear incident optical fluence. The high-order dependence, quantified by polynomial fitting, provides super-resolution imaging with optical sectioning. PA nanoscopy is capable of super-resolution imaging of either fluorescent or nonfluorescent molecules. PMID:25104412

Stimulated emission depletion microscopy resolves individual nitrogen vacancy centers in diamond nanocrystals.

PubMed

Arroyo-Camejo, Silvia; Adam, Marie-Pierre; Besbes, Mondher; Hugonin, Jean-Paul; Jacques, Vincent; Greffet, Jean-Jacques; Roch, Jean-François; Hell, Stefan W; Treussart, François

2013-12-23

Nitrogen-vacancy (NV) color centers in nanodiamonds are highly promising for bioimaging and sensing. However, resolving individual NV centers within nanodiamond particles and the controlled addressing and readout of their spin state has remained a major challenge. Spatially stochastic super-resolution techniques cannot provide this capability in principle, whereas coordinate-controlled super-resolution imaging methods, like stimulated emission depletion (STED) microscopy, have been predicted to fail in nanodiamonds. Here we show that, contrary to these predictions, STED can resolve single NV centers in 40-250 nm sized nanodiamonds with a resolution of ≈10 nm. Even multiple adjacent NVs located in single nanodiamonds can be imaged individually down to relative distances of ≈15 nm. Far-field optical super-resolution of NVs inside nanodiamonds is highly relevant for bioimaging applications of these fluorescent nanolabels. The targeted addressing and readout of individual NV(-) spins inside nanodiamonds by STED should also be of high significance for quantum sensing and information applications.

Usage of the Upgraded Vassilissa Separator for Synthesis of Super-Heavy Elements

NASA Astrophysics Data System (ADS)

Yeremin, A. V.; Malyshev, O. N.; Popeko, A. G.; Sagaidak, R. N.; Chepigin, V. I.; Kabachenko, A. P.; Belozerov, A. V.; Chelnokov, M. L.; Gorshkov, V. A.; Svirikhin, A. I.; Korotkov, S. P.; Rohach, J.; Brida, I.; Berek, G.

2002-12-01

Electrostatic separator VASSILISSA is used for exploring complete fussion nuclear reactions. The magnetic analyzer, based on D37 dipole magnet, was installed after the second triplet of quadrupole lenses of the separator for the mass identification of evaporation residues. Mass identification is an powerful tool for identification of recoil atoms of super-heavy elements. The new detection system consisting of the time-of-fiight system and 32-strips position-sensitive detector array was installed in the focal plane of the separator. The mass resolution of the separator after upgrade was found to be about 2.5 %.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

Techniques for super-resolution microscopy using NV-diamond

NASA Astrophysics Data System (ADS)

Trifonov, Alexei; Glenn, David; Bar-Gill, Nir; Le Sage, David; Walsworth, Ronald

2011-05-01

We discuss the development and application of techniques for super-resolution microscopy using NV centers in diamond: stimulated emission depletion (STED), metastable ground state depletion (GSD), and stochastic optical reconstruction microscopy (STORM). NV centers do not bleach under optical excitation, are not biotoxic, and have long-lived electronic spin coherence and spin-state-dependent fluorescence. Thus NV-diamond has great potential as a fluorescent biomarker and as a magnetic biosensor.

Custom Super-Resolution Microscope for the Structural Analysis of Nanostructures

DTIC Science & Technology

2018-05-29

research community. As part of our validation of the new design approach, we performed two - color imaging of pairs of adjacent oligo probes hybridized...nanostructures and biological targets. Our microscope features a large field of view and custom optics that facilitate 3D imaging and enhanced contrast in...our imaging throughput by creating two microscopy platforms for high-throughput, super-resolution materials characterization, with the AO set-up being

Enhanced labeling density and whole-cell 3D dSTORM imaging by repetitive labeling of target proteins.

PubMed

Venkataramani, Varun; Kardorff, Markus; Herrmannsdörfer, Frank; Wieneke, Ralph; Klein, Alina; Tampé, Robert; Heilemann, Mike; Kuner, Thomas

2018-04-03

With continuing advances in the resolving power of super-resolution microscopy, the inefficient labeling of proteins with suitable fluorophores becomes a limiting factor. For example, the low labeling density achieved with antibodies or small molecule tags limits attempts to reveal local protein nano-architecture of cellular compartments. On the other hand, high laser intensities cause photobleaching within and nearby an imaged region, thereby further reducing labeling density and impairing multi-plane whole-cell 3D super-resolution imaging. Here, we show that both labeling density and photobleaching can be addressed by repetitive application of trisNTA-fluorophore conjugates reversibly binding to a histidine-tagged protein by a novel approach called single-epitope repetitive imaging (SERI). For single-plane super-resolution microscopy, we demonstrate that, after multiple rounds of labeling and imaging, the signal density is increased. Using the same approach of repetitive imaging, washing and re-labeling, we demonstrate whole-cell 3D super-resolution imaging compensated for photobleaching above or below the imaging plane. This proof-of-principle study demonstrates that repetitive labeling of histidine-tagged proteins provides a versatile solution to break the 'labeling barrier' and to bypass photobleaching in multi-plane, whole-cell 3D experiments.

Micelle-templated composite quantum dots for super-resolution imaging.

PubMed

Xu, Jianquan; Fan, Qirui; Mahajan, Kalpesh D; Ruan, Gang; Herrington, Andrew; Tehrani, Kayvan F; Kner, Peter; Winter, Jessica O

2014-05-16

Quantum dots (QDs) have tremendous potential for biomedical imaging, including super-resolution techniques that permit imaging below the diffraction limit. However, most QDs are produced via organic methods, and hence require surface treatment to render them water-soluble for biological applications. Previously, we reported a micelle-templating method that yields nanocomposites containing multiple core/shell ZnS-CdSe QDs within the same nanocarrier, increasing overall particle brightness and virtually eliminating QD blinking. Here, this technique is extended to the encapsulation of Mn-doped ZnSe QDs (Mn-ZnSe QDs), which have potential applications in super-resolution imaging as a result of the introduction of Mn(2+) dopant energy levels. The size, shape and fluorescence characteristics of these doped QD-micelles were compared to those of micelles created using core/shell ZnS-CdSe QDs (ZnS-CdSe QD-micelles). Additionally, the stability of both types of particles to photo-oxidation was investigated. Compared to commercial QDs, micelle-templated QDs demonstrated superior fluorescence intensity, higher signal-to-noise ratios, and greater stability against photo-oxidization,while reducing blinking. Additionally, the fluorescence of doped QD-micelles could be modulated from a bright 'on' state to a dark 'off' state, with a modulation depth of up to 76%, suggesting the potential of doped QD-micelles for applications in super-resolution imaging.

Blind image fusion for hyperspectral imaging with the directional total variation

NASA Astrophysics Data System (ADS)

Bungert, Leon; Coomes, David A.; Ehrhardt, Matthias J.; Rasch, Jennifer; Reisenhofer, Rafael; Schönlieb, Carola-Bibiane

2018-04-01

Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.

METRIC model for the estimation and mapping of evapotranspiration in a super intensive olive orchard in Southern Portugal

NASA Astrophysics Data System (ADS)

Pôças, Isabel; Nogueira, António; Paço, Teresa A.; Sousa, Adélia; Valente, Fernanda; Silvestre, José; Andrade, José A.; Santos, Francisco L.; Pereira, Luís S.; Allen, Richard G.

2013-04-01

Satellite-based surface energy balance models have been successfully applied to estimate and map evapotranspiration (ET). The METRICtm model, Mapping EvapoTranspiration at high Resolution using Internalized Calibration, is one of such models. METRIC has been widely used over an extensive range of vegetation types and applications, mostly focusing annual crops. In the current study, the single-layer-blended METRIC model was applied to Landsat5 TM and Landsat7 ETM+ images to produce estimates of evapotranspiration (ET) in a super intensive olive orchard in Southern Portugal. In sparse woody canopies as in olive orchards, some adjustments in METRIC application related to the estimation of vegetation temperature and of momentum roughness length and sensible heat flux (H) for tall vegetation must be considered. To minimize biases in H estimates due to uncertainties in the definition of momentum roughness length, the Perrier function based on leaf area index and tree canopy architecture, associated with an adjusted estimation of crop height, was used to obtain momentum roughness length estimates. Additionally, to minimize the biases in surface temperature simulations, due to soil and shadow effects, the computation of radiometric temperature considered a three-source condition, where Ts=fcTc+fshadowTshadow+fsunlitTsunlit. As such, the surface temperature (Ts), derived from the thermal band of the Landsat images, integrates the temperature of the canopy (Tc), the temperature of the shaded ground surface (Tshadow), and the temperature of the sunlit ground surface (Tsunlit), according to the relative fraction of vegetation (fc), shadow (fshadow) and sunlit (fsunlit) ground surface, respectively. As the sunlit canopies are the primary source of energy exchange, the effective temperature for the canopy was estimated by solving the three-source condition equation for Tc. To evaluate METRIC performance to estimate ET over the olive grove, several parameters derived from the algorithm were tested against data collected in the field, including eddy covariance ET, surface temperature over the canopy and soil temperature in shaded and sunlit conditions. Additionally, the results were also compared with results published in the literature. The information obtained so far revealed very interesting perspectives for the use of METRIC in the estimation and mapping of ET in super intensive olive orchards. Thereby, this approach might constitute a useful tool towards the improvement of the efficiency of irrigation water management in this crop. The study described is still under way, and thus further applications of METRIC algorithm to a larger number of images and to olive groves with different tree density are planned.

Diagonalization of complex symmetric matrices: Generalized Householder reflections, iterative deflation and implicit shifts

NASA Astrophysics Data System (ADS)

Noble, J. H.; Lubasch, M.; Stevens, J.; Jentschura, U. D.

2017-12-01

We describe a matrix diagonalization algorithm for complex symmetric (not Hermitian) matrices, A ̲ =A̲T, which is based on a two-step algorithm involving generalized Householder reflections based on the indefinite inner product 〈 u ̲ , v ̲ 〉 ∗ =∑iuivi. This inner product is linear in both arguments and avoids complex conjugation. The complex symmetric input matrix is transformed to tridiagonal form using generalized Householder transformations (first step). An iterative, generalized QL decomposition of the tridiagonal matrix employing an implicit shift converges toward diagonal form (second step). The QL algorithm employs iterative deflation techniques when a machine-precision zero is encountered "prematurely" on the super-/sub-diagonal. The algorithm allows for a reliable and computationally efficient computation of resonance and antiresonance energies which emerge from complex-scaled Hamiltonians, and for the numerical determination of the real energy eigenvalues of pseudo-Hermitian and PT-symmetric Hamilton matrices. Numerical reference values are provided.

Super long viewing distance light homogeneous emitting three-dimensional display

NASA Astrophysics Data System (ADS)

Liao, Hongen

2015-04-01

Three-dimensional (3D) display technology has continuously been attracting public attention with the progress in today's 3D television and mature display technologies. The primary characteristics of conventional glasses-free autostereoscopic displays, such as spatial resolution, image depths, and viewing angle, are often limited due to the use of optical lenses or optical gratings. We present a 3D display using MEMS-scanning-mechanism-based light homogeneous emitting (LHE) approach and demonstrate that the display can directly generate an autostereoscopic 3D image without the need for optical lenses or gratings. The generated 3D image has the advantages of non-aberration and a high-definition spatial resolution, making it the first to exhibit animated 3D images with image depth of six meters. Our LHE 3D display approach can be used to generate a natural flat-panel 3D display with super long viewing distance and alternative real-time image update.

Synthesis of a Far-Red Photoactivatable Silicon-Containing Rhodamine for Super-Resolution Microscopy.

PubMed

Grimm, Jonathan B; Klein, Teresa; Kopek, Benjamin G; Shtengel, Gleb; Hess, Harald F; Sauer, Markus; Lavis, Luke D

2016-01-26

The rhodamine system is a flexible framework for building small-molecule fluorescent probes. Changing N-substitution patterns and replacing the xanthene oxygen with a dimethylsilicon moiety can shift the absorption and fluorescence emission maxima of rhodamine dyes to longer wavelengths. Acylation of the rhodamine nitrogen atoms forces the molecule to adopt a nonfluorescent lactone form, providing a convenient method to make fluorogenic compounds. Herein, we take advantage of all of these structural manipulations and describe a novel photoactivatable fluorophore based on a Si-containing analogue of Q-rhodamine. This probe is the first example of a "caged" Si-rhodamine, exhibits higher photon counts compared to established localization microscopy dyes, and is sufficiently red-shifted to allow multicolor imaging. The dye is a useful label for super-resolution imaging and constitutes a new scaffold for far-red fluorogenic molecules. © 2015 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

An Airborne Conflict Resolution Approach Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Mondoloni, Stephane; Conway, Sheila

2001-01-01

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

Robust human detection, tracking, and recognition in crowded urban areas

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection. The human tracking process approach includes: 1) Color and intensity feature matching track candidate selection; 2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively; 3) Adaptive track gate size selection for reducing false tracking probability; and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with `Tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google- Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more than 100 human targets with averaged tracking period (time length) longer than the performance of the current state-of-the-art.

SUPER-RESOLUTION ULTRASOUND TOMOGRAPHY: A PRELIMINARY STUDY WITH A RING ARRAY

DOE Office of Scientific and Technical Information (OSTI.GOV)

HUANG, LIANJIE; SIMONETTI, FRANCESCO; DURIC, NEBOJSA

2007-01-18

Ultrasound tomography attempts to retrieve the structure of an objective by exploiting the interaction of acoustic waves with the object. A fundamental limit of ultrasound tomography is that features cannot be resolved if they are spaced less than {lambda}/2 apart, where {lambda} is wavelength of the probing wave, regardless of the degree of accuracy of the measurements. Therefore, since the attenuation of the probing wave with propagation distance increases as {lambda} decreases, resolution has to be traded against imaging depth. Recently, it has been shown that the {lambda}/2 limit is a consequence of the Born approximation (implicit in the imagingmore » algorithms currently employed) which neglects the distortion of the probing wavefield as it travels through the medium to be imaged. On the other hand, such a distortion, which is due to the multiple scattering phenomenon, can encode unlimited resolution in the radiating component of the scattered field. Previously, a resolution better than {lambda}/3 has been reported in these proceedings [F. Simonetti, pp. 126 (2006)] in the case of elastic wave probing. In this paper, they demonstrate experimentally a resolution better than {lambda}/4 for objects immersed in a water bth probed by means of a ring array which excites and detects pressure waves in a full view configuration.« less

Super-resolution study of polymer mobility fluctuations near c*.

PubMed

King, John T; Yu, Changqian; Wilson, William L; Granick, Steve

2014-09-23

Nanoscale dynamic heterogeneities in synthetic polymer solutions are detected using super-resolution optical microscopy. To this end, we map concentration fluctuations in polystyrene-toluene solutions with spatial resolution below the diffraction limit, focusing on critical fluctuations near the polymer overlap concentration, c*. Two-photon super-resolution microscopy was adapted to be applicable in an organic solvent, and a home-built STED-FCS system with stimulated emission depletion (STED) was used to perform fluorescence correlation spectroscopy (FCS). The polystyrene serving as the tracer probe (670 kg mol(-1), radius of gyration RG ≈ 35 nm, end-labeled with a bodipy derivative chromophore) was dissolved in toluene at room temperature (good solvent) and mixed with matrix polystyrene (3,840 kg mol(-1), RG ≈ 97 nm, Mw/Mn = 1.04) whose concentration was varied from dilute to more than 10c*. Whereas for dilute solutions the intensity-intensity correlation function follows a single diffusion process, it splits starting at c* to imply an additional relaxation process provided that the experimental focal area does not greatly exceed the polymer blob size. We identify the slower mode as self-diffusion and the increasingly rapid mode as correlated segment fluctuations that reflect the cooperative diffusion coefficient, Dcoop. These real-space measurements find quantitative agreement between correlation lengths inferred from dynamic measurements and those from determining the limit below which diffusion coefficients are independent of spot size. This study is considered to illustrate the potential of importing into polymer science the techniques of super-resolution imaging.

Super-resolution imaging and tracking of protein-protein interactions in sub-diffraction cellular space

NASA Astrophysics Data System (ADS)

Liu, Zhen; Xing, Dong; Su, Qian Peter; Zhu, Yun; Zhang, Jiamei; Kong, Xinyu; Xue, Boxin; Wang, Sheng; Sun, Hao; Tao, Yile; Sun, Yujie

2014-07-01

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein-protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB-EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB-EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB-EF-Tu interactions.

Super-resolution imaging and tracking of protein–protein interactions in sub-diffraction cellular space

PubMed Central

Liu, Zhen; Xing, Dong; Su, Qian Peter; Zhu, Yun; Zhang, Jiamei; Kong, Xinyu; Xue, Boxin; Wang, Sheng; Sun, Hao; Tao, Yile; Sun, Yujie

2014-01-01

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein–protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB–EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB–EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB–EF-Tu interactions. PMID:25030837

Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution

DOE PAGES

Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.; ...

2016-01-01

Here, we have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single moleculemore » super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet.« less

Observation of Eye Pattern on Super-Resolution Near-Field Structure Disk with Write-Strategy Technique

NASA Astrophysics Data System (ADS)

Fuji, Hiroshi; Kikukawa, Takashi; Tominaga, Junji

2004-07-01

Pit-edge recording at a density of 150 nm pits and spaces is carried out on a super-resolution near-field structure (super-RENS) disk with a platinum oxide layer. Pits are recorded and read using a 635-nm-wavelength laser and an objective lens with a 0.6 numerical aperture. We arrange laser pulses to correctly record the pits on the disk by a write-strategy technique. The laser-pulse figure includes a unit time of 0.25 T and intensities of Pw1, Pw2 and Pw3. After recording pits of various lengths, the observation of an eye pattern is achieved despite a pit smaller than the resolution limit. Furthermore, the eye pattern maintains its shape even though other pits fill the adjacent tracks at a track density of 600 nm. The disk can be used as a pit-edge recording system through a write-strategy technique.

Reconstitution radicicol containing apolipoprotein B lipoparticle and tracing its cell uptake process by super resolution fluorescent microscopy.

NASA Astrophysics Data System (ADS)

Lin, Chung Ching; Lin, Po-Yen; Chang, Chia-Ching

Apolipoprotein B (apoB) is the only protein of LDL. LDL delivers cholesterol, triacylglycerides and lipids to the target cells. Reconstitute apoB lipoparticle (rABL) will be an idea drug delivery vehicle for hydrophobic and amphiphilic materials delivery. It is challenged to renature ApoB into its functional state from denatured state. By using modified bile salt and radicicol (Rad) added over-critical refolding process, apoB can be restored into its native like state. The intrinsic fluorescence of apoB increased during the refolding process. Moreover, radicicol (Rad) molecules have been encapsulated into reconstitute rABL (Rad@rABL). To investigate the cell uptake mechanism of Rad@rABL, a super resolution ground state depletion (GSD) microscopy is used in this research. Fluorescence labeled Rad@rABL can be traced within the tumor cell. Key words: LDL, radicicol, protein refolding, super resolution microscopy.

Toward 10-km mesh global climate simulations

NASA Astrophysics Data System (ADS)

Ohfuchi, W.; Enomoto, T.; Takaya, K.; Yoshioka, M. K.

2002-12-01

An atmospheric general circulation model (AGCM) that runs very efficiently on the Earth Simulator (ES) was developed. The ES is a gigantic vector-parallel computer with the peak performance of 40 Tflops. The AGCM, named AFES (AGCM for ES), was based on the version 5.4.02 of an AGCM developed jointly by the Center for Climate System Research, the University of Tokyo and the Japanese National Institute for Environmental Sciences. The AFES was, however, totally rewritten in FORTRAN90 and MPI while the original AGCM was written in FORTRAN77 and not capable of parallel computing. The AFES achieved 26 Tflops (about 65 % of the peak performance of the ES) at resolution of T1279L96 (10-km horizontal resolution and 500-m vertical resolution in middle troposphere to lower stratosphere). Some results of 10- to 20-day global simulations will be presented. At this moment, only short-term simulations are possible due to data storage limitation. As ten tera flops computing is achieved, peta byte data storage are necessary to conduct climate-type simulations at this super-high resolution global simulations. Some possibilities for future research topics in global super-high resolution climate simulations will be discussed. Some target topics are mesoscale structures and self-organization of the Baiu-Meiyu front over Japan, cyclogenecsis over the North Pacific and typhoons around the Japan area. Also improvement in local precipitation with increasing horizontal resolution will be demonstrated.

Subpixel target detection and enhancement in hyperspectral images

NASA Astrophysics Data System (ADS)

Tiwari, K. C.; Arora, M.; Singh, D.

2011-06-01

Hyperspectral data due to its higher information content afforded by higher spectral resolution is increasingly being used for various remote sensing applications including information extraction at subpixel level. There is however usually a lack of matching fine spatial resolution data particularly for target detection applications. Thus, there always exists a tradeoff between the spectral and spatial resolutions due to considerations of type of application, its cost and other associated analytical and computational complexities. Typically whenever an object, either manmade, natural or any ground cover class (called target, endmembers, components or class) gets spectrally resolved but not spatially, mixed pixels in the image result. Thus, numerous manmade and/or natural disparate substances may occur inside such mixed pixels giving rise to mixed pixel classification or subpixel target detection problems. Various spectral unmixing models such as Linear Mixture Modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding abundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented that achieves subpixel target detection in hyperspectral images by adjusting spatial distribution of abundance fraction within a pixel. Results obtained at different resolutions indicate that super-resolution mapping may effectively aid subpixel target detection.

Effects of whispering gallery mode in microsphere super-resolution imaging

NASA Astrophysics Data System (ADS)

Zhou, Song; Deng, Yongbo; Zhou, Wenchao; Yu, Muxin; Urbach, H. P.; Wu, Yihui

2017-09-01

Whispering Gallery modes have been presented in microscopic glass spheres or toruses with many applications. In this paper, the possible approaches to enhance the imaging resolution by Whispering Gallery modes are discussed, including evanescent waves coupling, transformed and illustration by Whispering Gallery modes. It shows that the high-order scattering modes play the dominant role in the reconstructed virtual image when the Whispering Gallery modes exist. Furthermore, we find that the high image resolution of electric dipoles can be achieved, when the out-of-phase components exist from the illustration of Whispering Gallery modes. Those results of our simulation could contribute to the knowledge of microsphere-assisted super-resolution imaging and its potential applications.

A TCAS-II Resolution Advisory Detection Algorithm

NASA Technical Reports Server (NTRS)

Munoz, Cesar; Narkawicz, Anthony; Chamberlain, James

2013-01-01

The Traffic Alert and Collision Avoidance System (TCAS) is a family of airborne systems designed to reduce the risk of mid-air collisions between aircraft. TCASII, the current generation of TCAS devices, provides resolution advisories that direct pilots to maintain or increase vertical separation when aircraft distance and time parameters are beyond designed system thresholds. This paper presents a mathematical model of the TCASII Resolution Advisory (RA) logic that assumes accurate aircraft state information. Based on this model, an algorithm for RA detection is also presented. This algorithm is analogous to a conflict detection algorithm, but instead of predicting loss of separation, it predicts resolution advisories. It has been formally verified that for a kinematic model of aircraft trajectories, this algorithm completely and correctly characterizes all encounter geometries between two aircraft that lead to a resolution advisory within a given lookahead time interval. The RA detection algorithm proposed in this paper is a fundamental component of a NASA sense and avoid concept for the integration of Unmanned Aircraft Systems in civil airspace.

A Modified Subpulse SAR Processing Procedure Based on the Range-Doppler Algorithm for Synthetic Wideband Waveforms

PubMed Central

Lim, Byoung-Gyun; Woo, Jea-Choon; Lee, Hee-Young; Kim, Young-Soo

2008-01-01

Synthetic wideband waveforms (SWW) combine a stepped frequency CW waveform and a chirp signal waveform to achieve high range resolution without requiring a large bandwidth or the consequent very high sampling rate. If an efficient algorithm like the range-Doppler algorithm (RDA) is used to acquire the SAR images for synthetic wideband signals, errors occur due to approximations, so the images may not show the best possible result. This paper proposes a modified subpulse SAR processing algorithm for synthetic wideband signals which is based on RDA. An experiment with an automobile-based SAR system showed that the proposed algorithm is quite accurate with a considerable improvement in resolution and quality of the obtained SAR image. PMID:27873984

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

NASA Technical Reports Server (NTRS)

Worrall, Diana M. (Editor); Biemesderfer, Chris (Editor); Barnes, Jeannette (Editor)

1992-01-01

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

Full-field dual-color 100-nm super-resolution imaging reveals organization and dynamics of mitochondrial and ER networks.

PubMed

Brunstein, Maia; Wicker, Kai; Hérault, Karine; Heintzmann, Rainer; Oheim, Martin

2013-11-04

Most structured illumination microscopes use a physical or synthetic grating that is projected into the sample plane to generate a periodic illumination pattern. Albeit simple and cost-effective, this arrangement hampers fast or multi-color acquisition, which is a critical requirement for time-lapse imaging of cellular and sub-cellular dynamics. In this study, we designed and implemented an interferometric approach allowing large-field, fast, dual-color imaging at an isotropic 100-nm resolution based on a sub-diffraction fringe pattern generated by the interference of two colliding evanescent waves. Our all-mirror-based system generates illumination pat-terns of arbitrary orientation and period, limited only by the illumination aperture (NA = 1.45), the response time of a fast, piezo-driven tip-tilt mirror (10 ms) and the available fluorescence signal. At low µW laser powers suitable for long-period observation of life cells and with a camera exposure time of 20 ms, our system permits the acquisition of super-resolved 50 µm by 50 µm images at 3.3 Hz. The possibility it offers for rapidly adjusting the pattern between images is particularly advantageous for experiments that require multi-scale and multi-color information. We demonstrate the performance of our instrument by imaging mitochondrial dynamics in cultured cortical astrocytes. As an illustration of dual-color excitation dual-color detection, we also resolve interaction sites between near-membrane mitochondria and the endoplasmic reticulum. Our TIRF-SIM microscope provides a versatile, compact and cost-effective arrangement for super-resolution imaging, allowing the investigation of co-localization and dynamic interactions between organelles--important questions in both cell biology and neurophysiology.

Historical feature pattern extraction based network attack situation sensing algorithm.

PubMed

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

PubMed Central

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

Parallel implementation of the particle simulation method with dynamic load balancing: Toward realistic geodynamical simulation

NASA Astrophysics Data System (ADS)

Furuichi, M.; Nishiura, D.

2015-12-01

Fully Lagrangian methods such as Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) have been widely used to solve the continuum and particles motions in the computational geodynamics field. These mesh-free methods are suitable for the problems with the complex geometry and boundary. In addition, their Lagrangian nature allows non-diffusive advection useful for tracking history dependent properties (e.g. rheology) of the material. These potential advantages over the mesh-based methods offer effective numerical applications to the geophysical flow and tectonic processes, which are for example, tsunami with free surface and floating body, magma intrusion with fracture of rock, and shear zone pattern generation of granular deformation. In order to investigate such geodynamical problems with the particle based methods, over millions to billion particles are required for the realistic simulation. Parallel computing is therefore important for handling such huge computational cost. An efficient parallel implementation of SPH and DEM methods is however known to be difficult especially for the distributed-memory architecture. Lagrangian methods inherently show workload imbalance problem for parallelization with the fixed domain in space, because particles move around and workloads change during the simulation. Therefore dynamic load balance is key technique to perform the large scale SPH and DEM simulation. In this work, we present the parallel implementation technique of SPH and DEM method utilizing dynamic load balancing algorithms toward the high resolution simulation over large domain using the massively parallel super computer system. Our method utilizes the imbalances of the executed time of each MPI process as the nonlinear term of parallel domain decomposition and minimizes them with the Newton like iteration method. In order to perform flexible domain decomposition in space, the slice-grid algorithm is used. Numerical tests show that our approach is suitable for solving the particles with different calculation costs (e.g. boundary particles) as well as the heterogeneous computer architecture. We analyze the parallel efficiency and scalability on the super computer systems (K-computer, Earth simulator 3, etc.).