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Sample records for peeling wavelet denoising

  1. Birdsong Denoising Using Wavelets

    PubMed Central

    Priyadarshani, Nirosha; Marsland, Stephen; Castro, Isabel; Punchihewa, Amal

    2016-01-01

    Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings. PMID:26812391

  2. Parallel object-oriented, denoising system using wavelet multiresolution analysis

    DOEpatents

    Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.; Tang, Nu A.

    2005-04-12

    The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.

  3. Application of wavelet analysis in laser Doppler vibration signal denoising

    NASA Astrophysics Data System (ADS)

    Lan, Yu-fei; Xue, Hui-feng; Li, Xin-liang; Liu, Dan

    2010-10-01

    Large number of experiments show that, due to external disturbances, the measured surface is too rough and other factors make use of laser Doppler technique to detect the vibration signal contained complex information, low SNR, resulting in Doppler frequency shift signals unmeasured, can not be demodulated Doppler phase and so on. This paper first analyzes the laser Doppler signal model and feature in the vibration test, and studies the most commonly used three ways of wavelet denoising techniques: the modulus maxima wavelet denoising method, the spatial correlation denoising method and wavelet threshold denoising method. Here we experiment with the vibration signals and achieve three ways by MATLAB simulation. Processing results show that the wavelet modulus maxima denoising method at low laser Doppler vibration SNR, has an advantage for the signal which mixed with white noise and contained more singularities; the spatial correlation denoising method is more suitable for denoising the laser Doppler vibration signal which noise level is not very high, and has a better edge reconstruction capacity; wavelet threshold denoising method has a wide range of adaptability, computational efficiency, and good denoising effect. Specifically, in the wavelet threshold denoising method, we estimate the original noise variance by spatial correlation method, using an adaptive threshold denoising method, and make some certain amendments in practice. Test can be shown that, compared with conventional threshold denoising, this method is more effective to extract the feature of laser Doppler vibration signal.

  4. Denoising solar radiation data using coiflet wavelets

    SciTech Connect

    Karim, Samsul Ariffin Abdul Janier, Josefina B. Muthuvalu, Mohana Sundaram; Hasan, Mohammad Khatim; Sulaiman, Jumat; Ismail, Mohd Tahir

    2014-10-24

    Signal denoising and smoothing plays an important role in processing the given signal either from experiment or data collection through observations. Data collection usually was mixed between true data and some error or noise. This noise might be coming from the apparatus to measure or collect the data or human error in handling the data. Normally before the data is use for further processing purposes, the unwanted noise need to be filtered out. One of the efficient methods that can be used to filter the data is wavelet transform. Due to the fact that the received solar radiation data fluctuates according to time, there exist few unwanted oscillation namely noise and it must be filtered out before the data is used for developing mathematical model. In order to apply denoising using wavelet transform (WT), the thresholding values need to be calculated. In this paper the new thresholding approach is proposed. The coiflet2 wavelet with variation diminishing 4 is utilized for our purpose. From numerical results it can be seen clearly that, the new thresholding approach give better results as compare with existing approach namely global thresholding value.

  5. Raman spectral data denoising based on wavelet analysis

    NASA Astrophysics Data System (ADS)

    Chen, Chen; Peng, Fei; Cheng, Qinghua; Xu, Dahai

    2008-12-01

    Abstract As one kind of molecule scattering spectroscopy, Raman spectroscopy (RS) is characterized by the frequency excursion that can show the information of molecule. RS has a broad application in biological, chemical, environmental and industrial fields. But signals in Raman spectral analysis often have noise, which greatly influences the achievement of accurate analytical results. The de-noising of RS signals is an important part of spectral analysis. Wavelet transform has been established with the Fourier transform as a data-processing method in analytical fields. The main fields of application are related to de-noising, compression, variable reduction, and signal suppression. In de-noising of Raman Spectroscopy, wavelet is chosen to construct de-noising function because of its excellent properties. In this paper, bior wavelet is adopted to remove the noise in the Raman spectra. It eliminates noise obviously and the result is satisfying. This method can provide some bases for practical de-noising in Raman spectra.

  6. Denoising time-domain induced polarisation data using wavelet techniques

    NASA Astrophysics Data System (ADS)

    Deo, Ravin N.; Cull, James P.

    2016-05-01

    Time-domain induced polarisation (TDIP) methods are routinely used for near-surface evaluations in quasi-urban environments harbouring networks of buried civil infrastructure. A conventional technique for improving signal to noise ratio in such environments is by using analogue or digital low-pass filtering followed by stacking and rectification. However, this induces large distortions in the processed data. In this study, we have conducted the first application of wavelet based denoising techniques for processing raw TDIP data. Our investigation included laboratory and field measurements to better understand the advantages and limitations of this technique. It was found that distortions arising from conventional filtering can be significantly avoided with the use of wavelet based denoising techniques. With recent advances in full-waveform acquisition and analysis, incorporation of wavelet denoising techniques can further enhance surveying capabilities. In this work, we present the rationale for utilising wavelet denoising methods and discuss some important implications, which can positively influence TDIP methods.

  7. Wavelet Denoising of Mobile Radiation Data

    SciTech Connect

    Campbell, D B

    2008-10-31

    The FY08 phase of this project investigated the merits of video fusion as a method for mitigating the false alarms encountered by vehicle borne detection systems in an effort to realize performance gains associated with wavelet denoising. The fusion strategy exploited the significant correlations which exist between data obtained from radiation detectors and video systems with coincident fields of view. The additional information provided by optical systems can greatly increase the capabilities of these detection systems by reducing the burden of false alarms and through the generation of actionable information. The investigation into the use of wavelet analysis techniques as a means of filtering the gross-counts signal obtained from moving radiation detectors showed promise for vehicle borne systems. However, the applicability of these techniques to man-portable systems is limited due to minimal gains in performance over the rapid feedback available to system operators under walking conditions. Furthermore, the fusion of video holds significant promise for systems operating from vehicles or systems organized into stationary arrays; however, the added complexity and hardware required by this technique renders it infeasible for man-portable systems.

  8. Denoising seismic data using wavelet methods: a comparison study

    NASA Astrophysics Data System (ADS)

    Hloupis, G.; Vallianatos, F.

    2009-04-01

    In order to derive onset times, amplitudes or other useful characteristic from a seismogram, the usual denoising procedure involves the use of a linear pass-band filter. This family of filters is zero-phase and is useful according to phase properties but their efficiency is reduced when transients are existing near seismic signals. The alternative solution is the Wiener filter which focuses on the elimination of the mean square error between recorded and expected signal. Its main disadvantage is the assumption that signal and noise are stationary. This assumption does not hold for the seismic signals leading to denoising solutions that does not assume stationarity. Solutions based on Wavelet Transform proved effective for denoising problems across several areas. Here we present recent WT denoising methods (WDM) that will applied later to seismic sequences of Seismological Network of Crete. Wavelet denoising schemes have proved to be well adapted to several types of signals. For non-stationary signals, such as seismograms, the use of linear and non-linear wavelet denoising methods seems promising. The contribution of this study is a comparison for wavelet denoising methods suitable for seismic signals, which proved from previous studies their superiority against appropriate conventional filtering techniques. The importance of wavelet denoising methods relies on two facts: they recovered the seismic signals having fewer artifacts than conventional filters (for high SNR seismograms) and at the same time they can provide satisfactory representations (for detecting the earthquake's primary arrival) for low SNR seismograms or microearthquakes. The latter is very important for a possible development of an automatic procedure for the regular daily detection of small or non-regional earthquakes especially when the number of the stations is quite big. Initially, their performance is measured over a database of synthetic seismic signals in order to evaluate the better wavelet

  9. Impedance cardiography signal denoising using discrete wavelet transform.

    PubMed

    Chabchoub, Souhir; Mansouri, Sofienne; Salah, Ridha Ben

    2016-09-01

    Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal. The Haar, Daubechies (db2, db4, db6, and db8), Symlet (sym2, sym4, sym6, sym8) and Coiflet (coif2, coif3, coif4, coif5) wavelet families are tested and evaluated in order to select the most suitable denoising method. The wavelet family with best performance is compared with two denoising methods: one based on Savitzky-Golay filtering and the other based on median filtering. Each method is evaluated by means of the signal to noise ratio (SNR), the root mean square error (RMSE) and the percent difference root mean square (PRD). The results show that the Daubechies wavelet family (db8) has superior performance on noise reduction in comparison to other methods. PMID:27376722

  10. Multitaper Spectral Analysis and Wavelet Denoising Applied to Helioseismic Data

    NASA Technical Reports Server (NTRS)

    Komm, R. W.; Gu, Y.; Hill, F.; Stark, P. B.; Fodor, I. K.

    1999-01-01

    Estimates of solar normal mode frequencies from helioseismic observations can be improved by using Multitaper Spectral Analysis (MTSA) to estimate spectra from the time series, then using wavelet denoising of the log spectra. MTSA leads to a power spectrum estimate with reduced variance and better leakage properties than the conventional periodogram. Under the assumption of stationarity and mild regularity conditions, the log multitaper spectrum has a statistical distribution that is approximately Gaussian, so wavelet denoising is asymptotically an optimal method to reduce the noise in the estimated spectra. We find that a single m-upsilon spectrum benefits greatly from MTSA followed by wavelet denoising, and that wavelet denoising by itself can be used to improve m-averaged spectra. We compare estimates using two different 5-taper estimates (Stepian and sine tapers) and the periodogram estimate, for GONG time series at selected angular degrees l. We compare those three spectra with and without wavelet-denoising, both visually, and in terms of the mode parameters estimated from the pre-processed spectra using the GONG peak-fitting algorithm. The two multitaper estimates give equivalent results. The number of modes fitted well by the GONG algorithm is 20% to 60% larger (depending on l and the temporal frequency) when applied to the multitaper estimates than when applied to the periodogram. The estimated mode parameters (frequency, amplitude and width) are comparable for the three power spectrum estimates, except for modes with very small mode widths (a few frequency bins), where the multitaper spectra broadened the modest compared with the periodogram. We tested the influence of the number of tapers used and found that narrow modes at low n values are broadened to the extent that they can no longer be fit if the number of tapers is too large. For helioseismic time series of this length and temporal resolution, the optimal number of tapers is less than 10.

  11. Application of Wavelet Analysis Technique in the Signal Denoising of Life Sign Detection

    NASA Astrophysics Data System (ADS)

    Zhen, Zhang; Fang, LIU.

    In life sign detection, radar echo signal is very weak and hard to extract. For solve this problem, weak life signal de-noising based on wavelet transform is studied. Through the studies of wavelet threshold de-noising method, the use of it in weak life signal de-noising in strong noise background, and the verification of simulation by Matlab, the results shows that wavelet threshold de-noising method can remove the noise signal from weak life signal effectively and be an effective de-noising and extraction method for weak life signal.

  12. Examining Alternatives to Wavelet Denoising for Astronomical Source Finding

    NASA Astrophysics Data System (ADS)

    Jurek, R.; Brown, S.

    2012-08-01

    The Square Kilometre Array and its pathfinders ASKAP and MeerKAT will produce prodigious amounts of data that necessitate automated source finding. The performance of automated source finders can be improved by pre-processing a dataset. In preparation for the WALLABY and DINGO surveys, we have used a test HI datacube constructed from actual Westerbork Telescope noise and WHISP HI galaxies to test the real world improvement of linear smoothing, the Duchamp source finder's wavelet denoising, iterative median smoothing and mathematical morphology subtraction, on intensity threshold source finding of spectral line datasets. To compare these pre-processing methods we have generated completeness-reliability performance curves for each method and a range of input parameters. We find that iterative median smoothing produces the best source finding results for ASKAP HI spectral line observations, but wavelet denoising is a safer pre-processing technique. In this paper we also present our implementations of iterative median smoothing and mathematical morphology subtraction.

  13. Electrocardiogram signal denoising based on a new improved wavelet thresholding

    NASA Astrophysics Data System (ADS)

    Han, Guoqiang; Xu, Zhijun

    2016-08-01

    Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

  14. Electrocardiogram signal denoising based on a new improved wavelet thresholding.

    PubMed

    Han, Guoqiang; Xu, Zhijun

    2016-08-01

    Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method. PMID:27587134

  15. [An improved wavelet threshold algorithm for ECG denoising].

    PubMed

    Liu, Xiuling; Qiao, Lei; Yang, Jianli; Dong, Bin; Wang, Hongrui

    2014-06-01

    Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones. PMID:25219225

  16. Optimization of dynamic measurement of receptor kinetics by wavelet denoising.

    PubMed

    Alpert, Nathaniel M; Reilhac, Anthonin; Chio, Tat C; Selesnick, Ivan

    2006-04-01

    The most important technical limitation affecting dynamic measurements with PET is low signal-to-noise ratio (SNR). Several reports have suggested that wavelet processing of receptor kinetic data in the human brain can improve the SNR of parametric images of binding potential (BP). However, it is difficult to fully assess these reports because objective standards have not been developed to measure the tradeoff between accuracy (e.g. degradation of resolution) and precision. This paper employs a realistic simulation method that includes all major elements affecting image formation. The simulation was used to derive an ensemble of dynamic PET ligand (11C-raclopride) experiments that was subjected to wavelet processing. A method for optimizing wavelet denoising is presented and used to analyze the simulated experiments. Using optimized wavelet denoising, SNR of the four-dimensional PET data increased by about a factor of two and SNR of three-dimensional BP maps increased by about a factor of 1.5. Analysis of the difference between the processed and unprocessed means for the 4D concentration data showed that more than 80% of voxels in the ensemble mean of the wavelet processed data deviated by less than 3%. These results show that a 1.5x increase in SNR can be achieved with little degradation of resolution. This corresponds to injecting about twice the radioactivity, a maneuver that is not possible in human studies without saturating the PET camera and/or exposing the subject to more than permitted radioactivity.

  17. Energy-based wavelet de-noising of hydrologic time series.

    PubMed

    Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu

    2014-01-01

    De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed.

  18. Denoising portal images by means of wavelet techniques

    NASA Astrophysics Data System (ADS)

    Gonzalez Lopez, Antonio Francisco

    Portal images are used in radiotherapy for the verification of patient positioning. The distinguishing feature of this image type lies in its formation process: the same beam used for patient treatment is used for image formation. The high energy of the photons used in radiotherapy strongly limits the quality of portal images: Low contrast between tissues, low spatial resolution and low signal to noise ratio. This Thesis studies the enhancement of these images, in particular denoising of portal images. The statistical properties of portal images and noise are studied: power spectra, statistical dependencies between image and noise and marginal, joint and conditional distributions in the wavelet domain. Later, various denoising methods are applied to noisy portal images. Methods operating in the wavelet domain are the basis of this Thesis. In addition, the Wiener filter and the non local means filter (NLM), operating in the image domain, are used as a reference. Other topics studied in this Thesis are spatial resolution, wavelet processing and image processing in dosimetry in radiotherapy. In this regard, the spatial resolution of portal imaging systems is studied; a new method for determining the spatial resolution of the imaging equipments in digital radiology is presented; the calculation of the power spectrum in the wavelet domain is studied; reducing uncertainty in film dosimetry is investigated; a method for the dosimetry of small radiation fields with radiochromic film is presented; the optimal signal resolution is determined, as a function of the noise level and the quantization step, in the digitization process of films and the useful optical density range is set, as a function of the required uncertainty level, for a densitometric system. Marginal distributions of portal images are similar to those of natural images. This also applies to the statistical relationships between wavelet coefficients, intra-band and inter-band. These facts result in a better

  19. Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

    NASA Astrophysics Data System (ADS)

    Bartolomeo, L.; Zecca, M.; Sessa, S.; Lin, Z.; Mukaeda, Y.; Ishii, H.; Takanishi, Atsuo

    2011-06-01

    The surface Electromyography (sEMG) signal is affected by different sources of noises: current technology is considerably robust to the interferences of the power line or the cable motion artifacts, but still there are many limitations with the baseline and the movement artifact noise. In particular, these sources have frequency spectra that include also the low-frequency components of the sEMG frequency spectrum; therefore, a standard all-bandwidth filtering could alter important information. The Wavelet denoising method has been demonstrated to be a powerful solution in processing white Gaussian noise in biological signals. In this paper we introduce a new technique for the denoising of the sEMG signal: by using the baseline of the signal before the task, we estimate the thresholds to apply to the Wavelet thresholding procedure. The experiments have been performed on ten healthy subjects, by placing the electrodes on the Extensor Carpi Ulnaris and Triceps Brachii on right upper and lower arms, and performing a flexion and extension of the right wrist. An Inertial Measurement Unit, developed in our group, has been used to recognize the movements of the hands to segment the exercise and the pre-task baseline. Finally, we show better performances of the proposed method in term of noise cancellation and distortion of the signal, quantified by a new suggested indicator of denoising quality, compared to the standard Donoho technique.

  20. Wavelet Denoising of Mobile Radiation Data

    SciTech Connect

    Campbell, D; Lanier, R

    2007-10-29

    The investigation of wavelet analysis techniques as a means of filtering the gross-count signal obtained from radiation detectors has shown promise. These signals are contaminated with high frequency statistical noise and significantly varying background radiation levels. Wavelet transforms allow a signal to be split into its constituent frequency components without losing relative timing information. Initial simulations and an injection study have been performed. Additionally, acquisition and analysis software has been written which allowed the technique to be evaluated in real-time under more realistic operating conditions. The technique performed well when compared to more traditional triggering techniques with its performance primarily limited by false alarms due to prominent features in the signal. An initial investigation into the potential rejection and classification of these false alarms has also shown promise.

  1. Improved deadzone modeling for bivariate wavelet shrinkage-based image denoising

    NASA Astrophysics Data System (ADS)

    DelMarco, Stephen

    2016-05-01

    Modern image processing performed on-board low Size, Weight, and Power (SWaP) platforms, must provide high- performance while simultaneously reducing memory footprint, power consumption, and computational complexity. Image preprocessing, along with downstream image exploitation algorithms such as object detection and recognition, and georegistration, place a heavy burden on power and processing resources. Image preprocessing often includes image denoising to improve data quality for downstream exploitation algorithms. High-performance image denoising is typically performed in the wavelet domain, where noise generally spreads and the wavelet transform compactly captures high information-bearing image characteristics. In this paper, we improve modeling fidelity of a previously-developed, computationally-efficient wavelet-based denoising algorithm. The modeling improvements enhance denoising performance without significantly increasing computational cost, thus making the approach suitable for low-SWAP platforms. Specifically, this paper presents modeling improvements to the Sendur-Selesnick model (SSM) which implements a bivariate wavelet shrinkage denoising algorithm that exploits interscale dependency between wavelet coefficients. We formulate optimization problems for parameters controlling deadzone size which leads to improved denoising performance. Two formulations are provided; one with a simple, closed form solution which we use for numerical result generation, and the second as an integral equation formulation involving elliptic integrals. We generate image denoising performance results over different image sets drawn from public domain imagery, and investigate the effect of wavelet filter tap length on denoising performance. We demonstrate denoising performance improvement when using the enhanced modeling over performance obtained with the baseline SSM model.

  2. The application study of wavelet packet transformation in the de-noising of dynamic EEG data.

    PubMed

    Li, Yifeng; Zhang, Lihui; Li, Baohui; Wei, Xiaoyang; Yan, Guiding; Geng, Xichen; Jin, Zhao; Xu, Yan; Wang, Haixia; Liu, Xiaoyan; Lin, Rong; Wang, Quan

    2015-01-01

    This paper briefly describes the basic principle of wavelet packet analysis, and on this basis introduces the general principle of wavelet packet transformation for signal den-noising. The dynamic EEG data under +Gz acceleration is made a de-noising treatment by using wavelet packet transformation, and the de-noising effects with different thresholds are made a comparison. The study verifies the validity and application value of wavelet packet threshold method for the de-noising of dynamic EEG data under +Gz acceleration. PMID:26405863

  3. Class of Fibonacci-Daubechies-4-Haar wavelets with applicability to ECG denoising

    NASA Astrophysics Data System (ADS)

    Smith, Christopher B.; Agaian, Sos S.

    2004-05-01

    The presented paper introduces a new class of wavelets that includes the simplest Haar wavelet (Daubechies-2) as well as the Daubechies-4 wavelet. This class is shown to have several properties similar to the Daubechies wavelets. In application, the new class of wavelets has been shown to effectively denoise ECG signals. In addition, the paper introduces a new polynomial soft threshold technique for denoising through wavelet shrinkage. The polynomial soft threshold technique is able to represent a wide class of polynomial behaviors, including classical soft thresholding.

  4. Wavelet denoising of multiframe optical coherence tomography data

    PubMed Central

    Mayer, Markus A.; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf P.

    2012-01-01

    We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise. PMID:22435103

  5. GPU-based cone-beam reconstruction using wavelet denoising

    NASA Astrophysics Data System (ADS)

    Jin, Kyungchan; Park, Jungbyung; Park, Jongchul

    2012-03-01

    The scattering noise artifact resulted in low-dose projection in repetitive cone-beam CT (CBCT) scans decreases the image quality and lessens the accuracy of the diagnosis. To improve the image quality of low-dose CT imaging, the statistical filtering is more effective in noise reduction. However, image filtering and enhancement during the entire reconstruction process exactly may be challenging due to high performance computing. The general reconstruction algorithm for CBCT data is the filtered back-projection, which for a volume of 512×512×512 takes up to a few minutes on a standard system. To speed up reconstruction, massively parallel architecture of current graphical processing unit (GPU) is a platform suitable for acceleration of mathematical calculation. In this paper, we focus on accelerating wavelet denoising and Feldkamp-Davis-Kress (FDK) back-projection using parallel processing on GPU, utilize compute unified device architecture (CUDA) platform and implement CBCT reconstruction based on CUDA technique. Finally, we evaluate our implementation on clinical tooth data sets. Resulting implementation of wavelet denoising is able to process a 1024×1024 image within 2 ms, except data loading process, and our GPU-based CBCT implementation reconstructs a 512×512×512 volume from 400 projection data in less than 1 minute.

  6. Microarray image enhancement by denoising using stationary wavelet transform.

    PubMed

    Wang, X H; Istepanian, Robert S H; Song, Yong Hua

    2003-12-01

    Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.

  7. Pulsar Signal Denoising Method Based on Laplace Distribution in No-subsampling Wavelet Packet Domain

    NASA Astrophysics Data System (ADS)

    Wenbo, Wang; Yanchao, Zhao; Xiangli, Wang

    2016-11-01

    In order to improve the denoising effect of the pulsar signal, a new denoising method is proposed in the no-subsampling wavelet packet domain based on the local Laplace prior model. First, we count the true noise-free pulsar signal’s wavelet packet coefficient distribution characteristics and construct the true signal wavelet packet coefficients’ Laplace probability density function model. Then, we estimate the denosied wavelet packet coefficients by using the noisy pulsar wavelet coefficients based on maximum a posteriori criteria. Finally, we obtain the denoisied pulsar signal through no-subsampling wavelet packet reconstruction of the estimated coefficients. The experimental results show that the proposed method performs better when calculating the pulsar time of arrival than the translation-invariant wavelet denoising method.

  8. ECG signals denoising using wavelet transform and independent component analysis

    NASA Astrophysics Data System (ADS)

    Liu, Manjin; Hui, Mei; Liu, Ming; Dong, Liquan; Zhao, Zhu; Zhao, Yuejin

    2015-08-01

    A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.

  9. Forecasting performance of denoising signal by Wavelet and Fourier Transforms using SARIMA model

    NASA Astrophysics Data System (ADS)

    Ismail, Mohd Tahir; Mamat, Siti Salwana; Hamzah, Firdaus Mohamad; Karim, Samsul Ariffin Abdul

    2014-07-01

    The goal of this research is to determine the forecasting performance of denoising signal. Monthly rainfall and monthly number of raindays with duration of 20 years (1990-2009) from Bayan Lepas station are utilized as the case study. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used in this research to find the denoise signal. The denoise data obtained by Fast Fourier Transform and Wavelet Transform are being analyze by seasonal ARIMA model. The best fitted model is determined by the minimum value of MSE. The result indicates that Wavelet Transform is an effective method in denoising the monthly rainfall and number of rain days signals compared to Fast Fourier Transform.

  10. Biomedical image and signal de-noising using dual tree complex wavelet transform

    NASA Astrophysics Data System (ADS)

    Rizi, F. Yousefi; Noubari, H. Ahmadi; Setarehdan, S. K.

    2011-10-01

    Dual tree complex wavelet transform(DTCWT) is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The purposes of de-noising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal or image. This paper proposes a method for removing white Gaussian noise from ECG signals and biomedical images. The discrete wavelet transform (DWT) is very valuable in a large scope of de-noising problems. However, it has limitations such as oscillations of the coefficients at a singularity, lack of directional selectivity in higher dimensions, aliasing and consequent shift variance. The complex wavelet transform CWT strategy that we focus on in this paper is Kingsbury's and Selesnick's dual tree CWT (DTCWT) which outperforms the critically decimated DWT in a range of applications, such as de-noising. Each complex wavelet is oriented along one of six possible directions, and the magnitude of each complex wavelet has a smooth bell-shape. In the final part of this paper, we present biomedical image and signal de-noising by the means of thresholding magnitude of the wavelet coefficients.

  11. [Near infrared spectra (NIR) analysis of octane number by wavelet denoising-derivative method].

    PubMed

    Tian, Gao-you; Yuan, Hong-fu; Chu, Xiao-li; Liu, Hui-ying; Lu, Wan-zhen

    2005-04-01

    Derivative can correct baseline effects and also increase the level of noise. Wavelet transform has been proven an efficient tool for de-noising. This paper is directed to the application of wavelet transfer and derivative in the NIR analysis of octane number (RON). The derivative parameters, as well as their effects on the noise level and analytic accuracy of RON, have been studied in detail. The results show that derivative can correct the baseline effects and increase the analytic accuracy. Noise from the derivative spectra has great detriment to the analysis of RON. De-noising of wavelet transform can increase the S/N and improve the analytical accuracy.

  12. Study on an improved wavelet threshold denoising for the time-resolved photoacoustic signals of the glucose solution

    NASA Astrophysics Data System (ADS)

    Ren, Zhong; Liu, Guodong; Huang, Zhen

    2015-08-01

    Although the time-domain method, frequency-domain method and wavelet-domain method can used into signal denoising or filtering, the denoising of blood glucose photoacoustic signals are limited due to some advantages. In this paper, an improved wavelet threshold denoising method is used to remove the noise of the blood glucose photoacoustic signals. In order to overcome some drawbacks of classical wavelet threshold denoising, an improved wavelet threshold function was proposed. In the simulation experiments, the different denoising results are compared between this improved wavelet threshold function and other functions. And the experiments of this improved wavelet threshold function into the denoising of the time-resolved photoacoustic signals of glucose solution are performed. The experimental result verifies that the improved wavelet threshold function denoising is available. The improved wavelet threshold function has better flexibility than others due to the usage of two threshold values and two factors. So, the improved wavelet threshold function has the potential value in the denoising field of blood glucose photoacoustic signals.

  13. Autocorrelation based denoising of manatee vocalizations using the undecimated discrete wavelet transform.

    PubMed

    Gur, Berke M; Niezrecki, Christopher

    2007-07-01

    Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations.

  14. Autocorrelation based denoising of manatee vocalizations using the undecimated discrete wavelet transform.

    PubMed

    Gur, Berke M; Niezrecki, Christopher

    2007-07-01

    Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations. PMID:17614478

  15. [Adaptive de-noising of ECG signal based on stationary wavelet transform].

    PubMed

    Dong, Hong-sheng; Zhang, Ai-hua; Hao, Xiao-hong

    2009-03-01

    According to the limitations of wavelet threshold in de-noising method, we approached a combining algorithm of the stationary wavelet transform with adaptive filter. The stationary wavelet transformation can suppress Gibbs phenomena in traditional DWT effectively, and adaptive filter is introduced at the high scale wavelet coefficient of the stationary wavelet transformation. It would remove baseline wander and keep the shape of low frequency and low amplitude P wave, T wave and ST segment wave of ECG signal well. That is important for analyzing ECG signal of other feature information.

  16. Study on an improved wavelet shift-invariant threshold denoising for pulsed laser induced glucose photoacoustic signals

    NASA Astrophysics Data System (ADS)

    Wang, Zhengzi; Ren, Zhong; Liu, Guodong

    2015-10-01

    Noninvasive measurement of blood glucose concentration has become a hotspot research in the world due to its characteristic of convenient, rapid and non-destructive etc. The blood glucose concentration monitoring based on photoacoustic technique has attracted many attentions because the detected signal is ultrasonic signals rather than the photo signals. But during the acquisition of the photoacoustic signals of glucose, the photoacoustic signals are not avoid to be polluted by some factors, such as the pulsed laser, electronic noises and circumstance noises etc. These disturbances will impact the measurement accuracy of the glucose concentration, So, the denoising of the glucose photoacoustic signals is a key work. In this paper, a wavelet shift-invariant threshold denoising method is improved, and a novel wavelet threshold function is proposed. For the novel wavelet threshold function, two threshold values and two different factors are set, and the novel function is high order derivative and continuous, which can be looked as the compromise between the wavelet soft threshold denoising and hard threshold denoising. Simulation experimental results illustrate that, compared with other wavelet threshold denoising, this improved wavelet shift-invariant threshold denoising has higher signal-to-noise ratio(SNR) and smaller root mean-square error (RMSE) value. And this improved denoising also has better denoising effect than others. Therefore, this improved denoising has a certain of potential value in the denoising of glucose photoacoustic signals.

  17. Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV

    NASA Astrophysics Data System (ADS)

    Mortezanejad, R.; Gholami, A.

    2016-06-01

    One of the most crucial challenges in seismic data processing is the reduction of noise in the data or improving the signal-to-noise ratio (SNR). Wavelet- and curvelet-based denoising algorithms have become popular to address random noise attenuation for seismic sections. Wavelet basis, thresholding function, and threshold value are three key factors of such algorithms, having a profound effect on the quality of the denoised section. Therefore, given a signal, it is necessary to optimize the denoising operator over these factors to achieve the best performance. In this paper a general denoising algorithm is developed as a multi-variant (variable) filter which performs in multi-scale transform domains (e.g. wavelet and curvelet). In the wavelet domain this general filter is a function of the type of wavelet, characterized by its smoothness, thresholding rule, and threshold value, while in the curvelet domain it is only a function of thresholding rule and threshold value. Also, two methods, Stein’s unbiased risk estimate (SURE) and generalized cross validation (GCV), evaluated using a Monte Carlo technique, are utilized to optimize the algorithm in both wavelet and curvelet domains for a given seismic signal. The best wavelet function is selected from a family of fractional B-spline wavelets. The optimum thresholding rule is selected from general thresholding functions which contain the most well known thresholding functions, and the threshold value is chosen from a set of possible values. The results obtained from numerical tests show high performance of the proposed method in both wavelet and curvelet domains in comparison to conventional methods when denoising seismic data.

  18. Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

    2015-11-01

    The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

  19. Evaluation of Wavelet Denoising Methods for Small-Scale Joint Roughness Estimation Using Terrestrial Laser Scanning

    NASA Astrophysics Data System (ADS)

    Bitenc, M.; Kieffer, D. S.; Khoshelham, K.

    2015-08-01

    The precision of Terrestrial Laser Scanning (TLS) data depends mainly on the inherent random range error, which hinders extraction of small details from TLS measurements. New post processing algorithms have been developed that reduce or eliminate the noise and therefore enable modelling details at a smaller scale than one would traditionally expect. The aim of this research is to find the optimum denoising method such that the corrected TLS data provides a reliable estimation of small-scale rock joint roughness. Two wavelet-based denoising methods are considered, namely Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), in combination with different thresholding procedures. The question is, which technique provides a more accurate roughness estimates considering (i) wavelet transform (SWT or DWT), (ii) thresholding method (fixed-form or penalised low) and (iii) thresholding mode (soft or hard). The performance of denoising methods is tested by two analyses, namely method noise and method sensitivity to noise. The reference data are precise Advanced TOpometric Sensor (ATOS) measurements obtained on 20 × 30 cm rock joint sample, which are for the second analysis corrupted by different levels of noise. With such a controlled noise level experiments it is possible to evaluate the methods' performance for different amounts of noise, which might be present in TLS data. Qualitative visual checks of denoised surfaces and quantitative parameters such as grid height and roughness are considered in a comparative analysis of denoising methods. Results indicate that the preferred method for realistic roughness estimation is DWT with penalised low hard thresholding.

  20. A criterion for signal-based selection of wavelets for denoising intrafascicular nerve recordings.

    PubMed

    Kamavuako, Ernest Nlandu; Jensen, Winnie; Yoshida, Ken; Kurstjens, Mathijs; Farina, Dario

    2010-02-15

    In this paper we propose a novel method for denoising intrafascicular nerve signals with the aim of improving action potential (AP) detection. The method is based on the stationary wavelet transform and thresholding of the wavelet coefficients. Since the choice of the mother wavelet substantially impact the performance, a criterion is proposed for selecting the optimal wavelet. The criterion for selection was based on the root mean square of the average of the output signal triggered by the detected APs. The mother wavelet was parameterized through the scaling filter, which allowed optimization through the proposed criterion. The method was tested on simulated signals and on experimental neural recordings. Experimental signals were recorded from the tibial branch of the sciatic nerve of three anaesthetized New Zealand white rabbits during controlled muscle stretches. The simulation results showed that the proposed method had an equivalent effect on AP detection performance (percentage of correct detection at 6 dB signal-to-noise ratio, mean+/-SD, 95.3+/-5.2%) to the a-posteriori choice of the best wavelet (96.1+/-3.6). Moreover, the AP detection after the proposed denoising method resulted in a correlation of 0.94+/-0.02 between the estimated spike rate and the muscle length. Therefore, the study proposes an effective method for selecting the optimal mother wavelet for denoising neural signals with the aim of improving AP detection.

  1. [Wavelet analysis and its application in denoising the spectrum of hyperspectral image].

    PubMed

    Zhou, Dan; Wang, Qin-Jun; Tian, Qing-Jiu; Lin, Qi-Zhong; Fu, Wen-Xue

    2009-07-01

    In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible. In the experiment, noise with signal to noise ratio (SNR) 30 was first added to the spectrum, and then removed by the wavelet denoising approach. For the purpose of finding the optimal parameters combinations, the SNR, mean squared error (MSE), spectral angle (SA) and integrated evaluation coefficient eta were used to evaluate the approach's denoising effects. Denoising effect is directly proportional to SNR, and inversely proportional to MSE, SA and the integrated evaluation coefficient eta. Denoising results show that the sawtoothed noise in noisy spectrum was basically eliminated, and the denoised spectrum basically coincides with the original spectrum, maintaining a good spectral characteristic of the curve. Evaluation results show that the optimal denoising can be achieved by firstly decomposing the noisy spectrum into 3-7 levels using db12, db10, sym9 and sym6 wavelets, then processing the wavelet transform coefficients by soft-threshold functions, and finally estimating the thresholds by heursure threshold selection rule and rescaling using a single estimation of level noise based on first-level coefficients. However, this approach depends on the noise level, which means that for different noise level the optimal parameters combination is also diverse.

  2. Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning

    NASA Astrophysics Data System (ADS)

    Li, Hao; Ma, Yong; Liang, Kun; Tian, Yong; Wang, Rui

    2012-01-01

    Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.

  3. Robust 4D Flow Denoising Using Divergence-Free Wavelet Transform

    PubMed Central

    Ong, Frank; Uecker, Martin; Tariq, Umar; Hsiao, Albert; Alley, Marcus T; Vasanawala, Shreyas S.; Lustig, Michael

    2014-01-01

    Purpose To investigate four-dimensional flow denoising using the divergence-free wavelet (DFW) transform and compare its performance with existing techniques. Theory and Methods DFW is a vector-wavelet that provides a sparse representation of flow in a generally divergence-free field and can be used to enforce “soft” divergence-free conditions when discretization and partial voluming result in numerical nondivergence-free components. Efficient denoising is achieved by appropriate shrinkage of divergence-free wavelet and nondivergence-free coefficients. SureShrink and cycle spinning are investigated to further improve denoising performance. Results DFW denoising was compared with existing methods on simulated and phantom data and was shown to yield better noise reduction overall while being robust to segmentation errors. The processing was applied to in vivo data and was demonstrated to improve visualization while preserving quantifications of flow data. Conclusion DFW denoising of four-dimensional flow data was shown to reduce noise levels in flow data both quantitatively and visually. PMID:24549830

  4. Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

    PubMed Central

    Khullar, Siddharth; Michael, Andrew; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince D.

    2010-01-01

    We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional de-noising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the de-noised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of de-noised wavelet coefficients for each voxel. Given the decorrelated nature of these de-noised wavelet coefficients; it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules. First, the analysis module where we combine a new 3-D wavelet denoising approach with better signal separation properties of ICA in the wavelet domain, to yield an activation component that corresponds closely to the true underlying signal and is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic (ROC) curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false

  5. Total variation versus wavelet-based methods for image denoising in fluorescence lifetime imaging microscopy

    PubMed Central

    Chang, Ching-Wei; Mycek, Mary-Ann

    2014-01-01

    We report the first application of wavelet-based denoising (noise removal) methods to time-domain box-car fluorescence lifetime imaging microscopy (FLIM) images and compare the results to novel total variation (TV) denoising methods. Methods were tested first on artificial images and then applied to low-light live-cell images. Relative to undenoised images, TV methods could improve lifetime precision up to 10-fold in artificial images, while preserving the overall accuracy of lifetime and amplitude values of a single-exponential decay model and improving local lifetime fitting in live-cell images. Wavelet-based methods were at least 4-fold faster than TV methods, but could introduce significant inaccuracies in recovered lifetime values. The denoising methods discussed can potentially enhance a variety of FLIM applications, including live-cell, in vivo animal, or endoscopic imaging studies, especially under challenging imaging conditions such as low-light or fast video-rate imaging. PMID:22415891

  6. Hardware Design and Implementation of a Wavelet De-Noising Procedure for Medical Signal Preprocessing

    PubMed Central

    Chen, Szi-Wen; Chen, Yuan-Ho

    2015-01-01

    In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz. PMID:26501290

  7. Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

    PubMed

    Chen, Szi-Wen; Chen, Yuan-Ho

    2015-01-01

    In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz. PMID:26501290

  8. Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

    PubMed

    Chen, Szi-Wen; Chen, Yuan-Ho

    2015-01-01

    In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

  9. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

    PubMed

    Boix, Macarena; Cantó, Begoña

    2013-04-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

  10. Wavelet-based adaptive denoising and baseline correction for MALDI TOF MS.

    PubMed

    Shin, Hyunjin; Sampat, Mehul P; Koomen, John M; Markey, Mia K

    2010-06-01

    Proteomic profiling by MALDI TOF mass spectrometry (MS) is an effective method for identifying biomarkers from human serum/plasma, but the process is complicated by the presence of noise in the spectra. In MALDI TOF MS, the major noise source is chemical noise, which is defined as the interference from matrix material and its clusters. Because chemical noise is nonstationary and nonwhite, wavelet-based denoising is more effective than conventional noise reduction schemes based on Fourier analysis. However, current wavelet-based denoising methods for mass spectrometry do not fully consider the characteristics of chemical noise. In this article, we propose new wavelet-based high-frequency noise reduction and baseline correction methods that were designed based on the discrete stationary wavelet transform. The high-frequency noise reduction algorithm adaptively estimates the time-varying threshold for each frequency subband from multiple realizations of chemical noise and removes noise from mass spectra of samples using the estimated thresholds. The baseline correction algorithm computes the monotonically decreasing baseline in the highest approximation of the wavelet domain. The experimental results demonstrate that our algorithms effectively remove artifacts in mass spectra that are due to chemical noise while preserving informative features as compared to commonly used denoising methods.

  11. Bayesian wavelet-based image denoising using the Gauss-Hermite expansion.

    PubMed

    Rahman, S M Mahbubur; Ahmad, M Omair; Swamy, M N S

    2008-10-01

    The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.

  12. Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction.

    PubMed

    Holan, Scott H; Viator, John A

    2008-06-21

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples. PMID:18495977

  13. NOTE: Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

    NASA Astrophysics Data System (ADS)

    Holan, Scott H.; Viator, John A.

    2008-06-01

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

  14. Improved total variation algorithms for wavelet-based denoising

    NASA Astrophysics Data System (ADS)

    Easley, Glenn R.; Colonna, Flavia

    2007-04-01

    Many improvements of wavelet-based restoration techniques suggest the use of the total variation (TV) algorithm. The concept of combining wavelet and total variation methods seems effective but the reasons for the success of this combination have been so far poorly understood. We propose a variation of the total variation method designed to avoid artifacts such as oil painting effects and is more suited than the standard TV techniques to be implemented with wavelet-based estimates. We then illustrate the effectiveness of this new TV-based method using some of the latest wavelet transforms such as contourlets and shearlets.

  15. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains.

    PubMed

    Lahmiri, Salim

    2014-09-01

    Hybrid denoising models based on combining empirical mode decomposition (EMD) and discrete wavelet transform (DWT) were found to be effective in removing additive Gaussian noise from electrocardiogram (ECG) signals. Recently, variational mode decomposition (VMD) has been proposed as a multiresolution technique that overcomes some of the limits of the EMD. Two ECG denoising approaches are compared. The first is based on denoising in the EMD domain by DWT thresholding, whereas the second is based on noise reduction in the VMD domain by DWT thresholding. Using signal-to-noise ratio and mean of squared errors as performance measures, simulation results show that the VMD-DWT approach outperforms the conventional EMD-DWT. In addition, a non-local means approach used as a reference technique provides better results than the VMD-DWT approach. PMID:26609387

  16. Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

    NASA Astrophysics Data System (ADS)

    Janusek, D.; Kania, M.; Zaczek, R.; Zavala-Fernandez, H.; Zbieć, A.; Opolski, G.; Maniewski, R.

    2011-01-01

    T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant T-wave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

  17. Denoising of arterial and venous Doppler signals using discrete wavelet transform: effect on clinical parameters.

    PubMed

    Tokmakçi, Mahmut; Erdoğan, Nuri

    2009-05-01

    In this paper, the effects of a wavelet transform based denoising strategy on clinical Doppler parameters are analyzed. The study scheme included: (a) Acquisition of arterial and venous Doppler signals by sampling the audio output of an ultrasound scanner from 20 healthy volunteers, (b) Noise reduction via decomposition of the signals through discrete wavelet transform, (c) Spectral analysis of noisy and noise-free signals with short time Fourier transform, (d) Curve fitting to spectrograms, (e) Calculation of clinical Doppler parameters, (f) Statistical comparison of parameters obtained from noisy and noise-free signals. The decomposition level was selected as the highest level at which the maximum power spectral density and its corresponding frequency were preserved. In all subjects, noise-free spectrograms had smoother trace with less ripples. In both arterial and venous spectrograms, denoising resulted in a significant decrease in the maximum (systolic) and mean frequency, with no statistical difference in the minimum (diastolic) frequency. In arterial signals, this leads to a significant decrease in the calculated parameters such as Systolic/Diastolic Velocity Ratio, Resistivity Index, Pulsatility Index and Acceleration Time. Acceleration Index did not change significantly. Despite a successful denoising, the effects of wavelet decomposition on high frequency components in the Doppler signal should be challenged by comparison with reference data, or, through clinical investigations. PMID:19470316

  18. Wavelet-domain TI Wiener-like filtering for complex MR data denoising.

    PubMed

    Hu, Kai; Cheng, Qiaocui; Gao, Xieping

    2016-10-01

    Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness. PMID:27238055

  19. Wavelet-domain TI Wiener-like filtering for complex MR data denoising.

    PubMed

    Hu, Kai; Cheng, Qiaocui; Gao, Xieping

    2016-10-01

    Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness.

  20. Applications of wavelet analysis in differential propagation phase shift data de-noising

    NASA Astrophysics Data System (ADS)

    Hu, Zhiqun; Liu, Liping

    2014-07-01

    Using numerical simulation data of the forward differential propagation shift (ϕDP) of polarimetric radar, the principle and performing steps of noise reduction by wavelet analysis are introduced in detail. Profiting from the multiscale analysis, various types of noises can be identified according to their characteristics in different scales, and suppressed in different resolutions by a penalty threshold strategy through which a fixed threshold value is applied, a default threshold strategy through which the threshold value is determined by the noise intensity, or a ϕDP penalty threshold strategy through which a special value is designed for ϕDP de-noising. Then, a hard-or soft-threshold function, depending on the de-noising purpose, is selected to reconstruct the signal. Combining the three noise suppression strategies and the two signal reconstruction functions, and without loss of generality, two schemes are presented to verify the de-noising effect by dbN wavelets: (1) the penalty threshold strategy with the soft threshold function scheme (PSS); (2) the ϕDP penalty threshold strategy with the soft threshold function scheme (PPSS). Furthermore, the wavelet de-noising is compared with the mean, median, Kalman, and finite impulse response (FIR) methods with simulation data and two actual cases. The results suggest that both of the two schemes perform well, especially when ϕDP data are simultaneously polluted by various scales and types of noises. A slight difference is that the PSS method can retain more detail, and the PPSS can smooth the signal more successfully.

  1. The EM Method in a Probabilistic Wavelet-Based MRI Denoising

    PubMed Central

    2015-01-01

    Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images. PMID:26089959

  2. A Wiener-Wavelet-Based filter for de-noising satellite soil moisture retrievals

    NASA Astrophysics Data System (ADS)

    Massari, Christian; Brocca, Luca; Ciabatta, Luca; Moramarco, Tommaso; Su, Chun-Hsu; Ryu, Dongryeol; Wagner, Wolfgang

    2014-05-01

    The reduction of noise in microwave satellite soil moisture (SM) retrievals is of paramount importance for practical applications especially for those associated with the study of climate changes, droughts, floods and other related hydrological processes. So far, Fourier based methods have been used for de-noising satellite SM retrievals by filtering either the observed emissivity time series (Du, 2012) or the retrieved SM observations (Su et al. 2013). This contribution introduces an alternative approach based on a Wiener-Wavelet-Based filtering (WWB) technique, which uses the Entropy-Based Wavelet de-noising method developed by Sang et al. (2009) to design both a causal and a non-causal version of the filter. WWB is used as a post-retrieval processing tool to enhance the quality of observations derived from the i) Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), ii) the Advanced SCATterometer (ASCAT), and iii) the Soil Moisture and Ocean Salinity (SMOS) satellite. The method is tested on three pilot sites located in Spain (Remedhus Network), in Greece (Hydrological Observatory of Athens) and in Australia (Oznet network), respectively. Different quantitative criteria are used to judge the goodness of the de-noising technique. Results show that WWB i) is able to improve both the correlation and the root mean squared differences between satellite retrievals and in situ soil moisture observations, and ii) effectively separates random noise from deterministic components of the retrieved signals. Moreover, the use of WWB de-noised data in place of raw observations within a hydrological application confirms the usefulness of the proposed filtering technique. Du, J. (2012), A method to improve satellite soil moisture retrievals based on Fourier analysis, Geophys. Res. Lett., 39, L15404, doi:10.1029/ 2012GL052435 Su,C.-H.,D.Ryu, A. W. Western, and W. Wagner (2013), De-noising of passive and active microwave satellite soil moisture time

  3. Application of time-resolved glucose concentration photoacoustic signals based on an improved wavelet denoising

    NASA Astrophysics Data System (ADS)

    Ren, Zhong; Liu, Guodong; Huang, Zhen

    2014-10-01

    Real-time monitoring of blood glucose concentration (BGC) is a great important procedure in controlling diabetes mellitus and preventing the complication for diabetic patients. Noninvasive measurement of BGC has already become a research hotspot because it can overcome the physical and psychological harm. Photoacoustic spectroscopy is a well-established, hybrid and alternative technique used to determine the BGC. According to the theory of photoacoustic technique, the blood is irradiated by plused laser with nano-second repeation time and micro-joule power, the photoacoustic singals contained the information of BGC are generated due to the thermal-elastic mechanism, then the BGC level can be interpreted from photoacoustic signal via the data analysis. But in practice, the time-resolved photoacoustic signals of BGC are polluted by the varities of noises, e.g., the interference of background sounds and multi-component of blood. The quality of photoacoustic signal of BGC directly impacts the precision of BGC measurement. So, an improved wavelet denoising method was proposed to eliminate the noises contained in BGC photoacoustic signals. To overcome the shortcoming of traditional wavelet threshold denoising, an improved dual-threshold wavelet function was proposed in this paper. Simulation experimental results illustrated that the denoising result of this improved wavelet method was better than that of traditional soft and hard threshold function. To varify the feasibility of this improved function, the actual photoacoustic BGC signals were test, the test reslut demonstrated that the signal-to-noises ratio(SNR) of the improved function increases about 40-80%, and its root-mean-square error (RMSE) decreases about 38.7-52.8%.

  4. Neurochip based on light-addressable potentiometric sensor with wavelet transform de-noising*

    PubMed Central

    Liu, Qing-jun; Ye, Wei-wei; Yu, Hui; Hu, Ning; Du, Li-ping; Wang, Ping

    2010-01-01

    Neurochip based on light-addressable potentiometric sensor (LAPS), whose sensing elements are excitable cells, can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed. Here we report a kind of neurochip with rat pheochromocytoma (PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform. Cells were cultured on LAPS for several days to form networks, and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space. The signal was decomposed into various scales, and coefficients were processed based on the properties of each layer. At last, signal was reconstructed based on the new coefficients. The results show that after de-noising, baseline drift is removed and signal-to-noise ratio is increased. It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform, taking advantage of its time-frequency localization analysis to reduce noise. PMID:20443210

  5. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

    PubMed

    Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping

    2015-01-01

    Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

  6. A wavelet denoising approach for signal action isolation in the ear canal.

    PubMed

    Vaidyanathan, Ravi; Wang, Shouyan; Gupta, Lalit

    2008-01-01

    The goal of this work was to develop and implement a new filtering strategy to denoise acoustic signals in the ear canal resulting from voluntary movement of the tongue (as a method of generating control input), as well as from other active actions, (speech, eating, drinking, smoking), and passive actions (swallowing, adjusting the jaw, physiological activity). The strategy is based on a denoising wavelet shrinkage approach that separates rhythmic bursting activity and white noise representing sustained tonic activity. While past work has addressed the discrimination of voluntary TMEP signals from one-another, no work has addressed acoustic artefact rejection within the ear. The results described here, combined with our past work in isolating critical components of tongue movement ear pressure (TMEP) signals, provide a basis for discriminating voluntary and involuntary actions of the tongue by monitoring pressure in the ear. At this time, the system has worked in real-time for assistive device control.

  7. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

    PubMed Central

    Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping

    2015-01-01

    Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately. PMID:26413090

  8. Denoising of X-ray pulsar observed profile in the undecimated wavelet domain

    NASA Astrophysics Data System (ADS)

    Xue, Meng-fan; Li, Xiao-ping; Fu, Ling-zhong; Liu, Xiu-ping; Sun, Hai-feng; Shen, Li-rong

    2016-01-01

    The low intensity of the X-ray pulsar signal and the strong X-ray background radiation lead to low signal-to-noise ratio (SNR) of the X-ray pulsar observed profile obtained through epoch folding, especially when the observation time is not long enough. This signifies the necessity of denoising of the observed profile. In this paper, the statistical characteristics of the X-ray pulsar signal are studied, and a signal-dependent noise model is established for the observed profile. Based on this, a profile noise reduction method by performing a local linear minimum mean square error filtering in the un-decimated wavelet domain is developed. The detail wavelet coefficients are rescaled by multiplying their amplitudes by a locally adaptive factor, which is the local variance ratio of the noiseless coefficients to the noisy ones. All the nonstationary statistics needed in the algorithm are calculated from the observed profile, without a priori information. The results of experim! ents, carried out on simulated data obtained by the ground-based simulation system and real data obtained by Rossi X-Ray Timing Explorer satellite, indicate that the proposed method is excellent in both noise suppression and preservation of peak sharpness, and it also clearly outperforms four widely accepted and used wavelet denoising methods, in terms of SNR, Pearson correlation coefficient and root mean square error.

  9. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    Ye, Linlin; Yang, Dan; Wang, Xu

    2014-06-01

    A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal. PMID:25219236

  10. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    Ye, Linlin; Yang, Dan; Wang, Xu

    2014-06-01

    A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.

  11. Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

    NASA Astrophysics Data System (ADS)

    Sayadi, Omid; Shamsollahi, Mohammad B.

    2007-12-01

    We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the[InlineEquation not available: see fulltext.]-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

  12. Research on biochemical spectrum denoising based on a novel wavelet threshold function and an improved translation-invariance method

    NASA Astrophysics Data System (ADS)

    Ren, Zhong; Liu, Guodong; Zeng, Lvming; Huang, Zhen; Huang, Shuanggen

    2008-12-01

    In this paper, an improved wavelet threshold denoising with combined translation invariance(TI)method is adopted to remove noises existed in the bio-chemical spectrum. Meanwhile, a novel wavelet threshold function and an optimal threshold determination algorithm are proposed. The new function is continuous and high-order derivable, it can overcome the vibration phenomena generated by the classical threshold function and decrease the error of reconstructed spectrum. So, it is superior to the frequency-domain filtering methods, the soft- and hard-threshold function proposed by D.L. Donoho and the semisoft-threshold function proposed by Gao, etc. The experimental results show that the improved TI wavelet threshold(TI-WT) denoising method can availably eliminate the Pseudo-Gibbs phenomena generated by the traditional wavelet thresholding method. At the same time, the improved wavelet threshold function and the TI-WT method present lower root mean-square-error (RMSE) and higher signal-to-noise ratio(SNR) than the frequency-domain filtering, classical soft and hard-threshold denoising The SNR increasing from 17.3200 to 32.5609, the RMSE decreasing from 4.0244 to 0.6257. Otherwise, The improved denoising method not only makes the spectrum smooth, but also effectively preserves the edge characteristics of the original spectrum.

  13. The application of wavelet shrinkage denoising to magnetic Barkhausen noise measurements

    SciTech Connect

    Thomas, James

    2014-02-18

    The application of Magnetic Barkhausen Noise (MBN) as a non-destructive method of defect detection has proliferated throughout the manufacturing community. Instrument technology and measurement methodology have matured commensurately as applications have moved from the R and D labs to the fully automated manufacturing environment. These new applications present a new set of challenges including a bevy of error sources. A significant obstacle in many industrial applications is a decrease in signal to noise ratio due to (i) environmental EMI and (II) compromises in sensor design for the purposes of automation. The stochastic nature of MBN presents a challenge to any method of noise reduction. An application of wavelet shrinkage denoising is proposed as a method of decreasing extraneous noise in MBN measurements. The method is tested and yields marked improvement on measurements subject to EMI, grounding noise, and even measurements in ideal conditions.

  14. A blind detection scheme based on modified wavelet denoising algorithm for wireless optical communications

    NASA Astrophysics Data System (ADS)

    Li, Ruijie; Dang, Anhong

    2015-10-01

    This paper investigates a detection scheme without channel state information for wireless optical communication (WOC) systems in turbulence induced fading channel. The proposed scheme can effectively diminish the additive noise caused by background radiation and photodetector, as well as the intensity scintillation caused by turbulence. The additive noise can be mitigated significantly using the modified wavelet threshold denoising algorithm, and then, the intensity scintillation can be attenuated by exploiting the temporal correlation of the WOC channel. Moreover, to improve the performance beyond that of the maximum likelihood decision, the maximum a posteriori probability (MAP) criterion is considered. Compared with conventional blind detection algorithm, simulation results show that the proposed detection scheme can improve the signal-to-noise ratio (SNR) performance about 4.38 dB while the bit error rate and scintillation index (SI) are 1×10-6 and 0.02, respectively.

  15. Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography.

    PubMed

    Arboleda, Carolina; Wang, Zhentian; Stampanoni, Marco

    2013-05-01

    Traditional mammography can be positively complemented by phase contrast and scattering x-ray imaging, because they can detect subtle differences in the electron density of a material and measure the local small-angle scattering power generated by the microscopic density fluctuations in the specimen, respectively. The grating-based x-ray interferometry technique can produce absorption, differential phase contrast (DPC) and scattering signals of the sample, in parallel, and works well with conventional X-ray sources; thus, it constitutes a promising method for more reliable breast cancer screening and diagnosis. Recently, our team proved that this novel technology can provide images superior to conventional mammography. This new technology was used to image whole native breast samples directly after mastectomy. The images acquired show high potential, but the noise level associated to the DPC and scattering signals is significant, so it is necessary to remove it in order to improve image quality and visualization. The noise models of the three signals have been investigated and the noise variance can be computed. In this work, a wavelet-based denoising algorithm using these noise models is proposed. It was evaluated with both simulated and experimental mammography data. The outcomes demonstrated that our method offers a good denoising quality, while simultaneously preserving the edges and important structural features. Therefore, it can help improve diagnosis and implement further post-processing techniques such as fusion of the three signals acquired.

  16. Non parametric denoising methods based on wavelets: Application to electron microscopy images in low exposure time

    SciTech Connect

    Soumia, Sid Ahmed; Messali, Zoubeida; Ouahabi, Abdeldjalil; Trepout, Sylvain E-mail: cedric.messaoudi@curie.fr Messaoudi, Cedric E-mail: cedric.messaoudi@curie.fr Marco, Sergio E-mail: cedric.messaoudi@curie.fr

    2015-01-13

    The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the

  17. Wavelet Transform-Based De-Noising for Two-Photon Imaging of Synaptic Ca2+ Transients

    PubMed Central

    Tigaret, Cezar M.; Tsaneva-Atanasova, Krasimira; Collingridge, Graham L.; Mellor, Jack R.

    2013-01-01

    Postsynaptic Ca2+ transients triggered by neurotransmission at excitatory synapses are a key signaling step for the induction of synaptic plasticity and are typically recorded in tissue slices using two-photon fluorescence imaging with Ca2+-sensitive dyes. The signals generated are small with very low peak signal/noise ratios (pSNRs) that make detailed analysis problematic. Here, we implement a wavelet-based de-noising algorithm (PURE-LET) to enhance signal/noise ratio for Ca2+ fluorescence transients evoked by single synaptic events under physiological conditions. Using simulated Ca2+ transients with defined noise levels, we analyzed the ability of the PURE-LET algorithm to retrieve the underlying signal. Fitting single Ca2+ transients with an exponential rise and decay model revealed a distortion of τrise but improved accuracy and reliability of τdecay and peak amplitude after PURE-LET de-noising compared to raw signals. The PURE-LET de-noising algorithm also provided a ∼30-dB gain in pSNR compared to ∼16-dB pSNR gain after an optimized binomial filter. The higher pSNR provided by PURE-LET de-noising increased discrimination accuracy between successes and failures of synaptic transmission as measured by the occurrence of synaptic Ca2+ transients by ∼20% relative to an optimized binomial filter. Furthermore, in comparison to binomial filter, no optimization of PURE-LET de-noising was required for reducing arbitrary bias. In conclusion, the de-noising of fluorescent Ca2+ transients using PURE-LET enhances detection and characterization of Ca2+ responses at central excitatory synapses. PMID:23473483

  18. Nonlinear denoising of functional magnetic resonance imaging time series with wavelets

    NASA Astrophysics Data System (ADS)

    Stausberg, Sven; Lehnertz, Klaus

    2009-04-01

    In functional magnetic resonance imaging (fMRI) the blood oxygenation level dependent (BOLD) effect is used to identify and delineate neuronal activity. The sensitivity of a fMRI-based detection of neuronal activation, however, strongly depends on the relative levels of signal and noise in the time series data, and a large number of different artifact and noise sources interfere with the weak signal changes of the BOLD response. Thus, noise reduction is important to allow an accurate estimation of single activation-related BOLD signals across brain regions. Techniques employed so far include filtering in the time or frequency domain which, however, does not take into account possible nonlinearities of the BOLD response. We here evaluate a previously proposed method for nonlinear denoising of short and transient signals, which combines the wavelet transform with techniques from nonlinear time series analysis. We adopt the method to the problem at hand and show that successful noise reduction and, more importantly, preservation of the shape of individual BOLD signals can be achieved even in the presence of in-band noise.

  19. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    PubMed

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.

  20. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    PubMed

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. PMID:26753616

  1. Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

    PubMed Central

    2016-01-01

    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

  2. Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models.

    PubMed

    Chan Phooi M'ng, Jacinta; Mehralizadeh, Mohammadali

    2016-01-01

    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today's increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong's Hang Seng futures, Japan's NIKKEI 225 futures, Singapore's MSCI futures, South Korea's KOSPI 200 futures, and Taiwan's TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.

  3. Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models.

    PubMed

    Chan Phooi M'ng, Jacinta; Mehralizadeh, Mohammadali

    2016-01-01

    The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today's increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong's Hang Seng futures, Japan's NIKKEI 225 futures, Singapore's MSCI futures, South Korea's KOSPI 200 futures, and Taiwan's TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692

  4. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    SciTech Connect

    Na, Man Gyun; Oh, Seungrohk

    2002-11-15

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.

  5. Analysis of hydrological trend for radioactivity content in bore-hole water samples using wavelet based denoising.

    PubMed

    Paul, Sabyasachi; Suman, V; Sarkar, P K; Ranade, A K; Pulhani, V; Dafauti, S; Datta, D

    2013-08-01

    A wavelet transform based denoising methodology has been applied to detect the presence of any discernable trend in (137)Cs and (90)Sr activity levels in bore-hole water samples collected four times a year over a period of eight years, from 2002 to 2009, in the vicinity of typical nuclear facilities inside the restricted access zones. The conventional non-parametric methods viz., Mann-Kendall and Spearman rho, along with linear regression when applied for detecting the linear trend in the time series data do not yield results conclusive for trend detection with a confidence of 95% for most of the samples. The stationary wavelet based hard thresholding data pruning method with Haar as the analyzing wavelet was applied to remove the noise present in the same data. Results indicate that confidence interval of the established trend has significantly improved after pre-processing to more than 98% compared to the conventional non-parametric methods when applied to direct measurements.

  6. Enhanced Terahertz Imaging of Small Forced Delamination in Woven Glass Fibre-reinforced Composites with Wavelet De-noising

    NASA Astrophysics Data System (ADS)

    Dong, Junliang; Locquet, Alexandre; Citrin, D. S.

    2016-03-01

    Terahertz (THz) reflection imaging is applied to characterize a woven glass fibre-reinforced composite laminate with a small region of forced delamination. The forced delamination is created by inserting a disk of 25- μ m-thick Upilex film, which is below the THz axial resolution, resulting in one featured echo with small amplitude in the reflected THz pulses. Low-amplitude components of the temporal signal due to ambient water vapor produce features of comparable amplitude with features associated with the THz pulse reflected off the interfaces of the delamination and suppress the contrast of THz C- and B-scans. Wavelet shrinkage de-noising is performed to remove water-vapor features, leading to enhanced THz C- and B-scans to locate the delamination in three dimensions with high contrast.

  7. Classical low-pass filter and real-time wavelet-based denoising technique implemented on a DSP: a comparison study

    NASA Astrophysics Data System (ADS)

    Dolabdjian, Ch.; Fadili, J.; Huertas Leyva, E.

    2002-11-01

    We have implemented a real-time numerical denoising algorithm, using the Discrete Wavelet Transform (DWT), on a TMS320C3x Digital Signal Processor (DSP). We also compared from a theoretical and practical viewpoints this post-processing approach to a more classical low-pass filter. This comparison was carried out using an ECG-type signal (ElectroCardiogram). The denoising approach is an elegant and extremely fast alternative to the classical linear filters class. It is particularly adapted to non-stationary signals such as those encountered in biological applications. The denoising allows to substantially improve detection of such signals over Fourier-based techniques. This processing step is a vital element in our acquisition chain using high sensitivity magnetic sensors. It should enhance detection of cardiac-type magnetic signals or magnetic particles in movement.

  8. Wavelet-based denoising of the Fourier metric in real-time wavefront correction for single molecule localization microscopy

    NASA Astrophysics Data System (ADS)

    Tehrani, Kayvan Forouhesh; Mortensen, Luke J.; Kner, Peter

    2016-03-01

    Wavefront sensorless schemes for correction of aberrations induced by biological specimens require a time invariant property of an image as a measure of fitness. Image intensity cannot be used as a metric for Single Molecule Localization (SML) microscopy because the intensity of blinking fluorophores follows exponential statistics. Therefore a robust intensity-independent metric is required. We previously reported a Fourier Metric (FM) that is relatively intensity independent. The Fourier metric has been successfully tested on two machine learning algorithms, a Genetic Algorithm and Particle Swarm Optimization, for wavefront correction about 50 μm deep inside the Central Nervous System (CNS) of Drosophila. However, since the spatial frequencies that need to be optimized fall into regions of the Optical Transfer Function (OTF) that are more susceptible to noise, adding a level of denoising can improve performance. Here we present wavelet-based approaches to lower the noise level and produce a more consistent metric. We compare performance of different wavelets such as Daubechies, Bi-Orthogonal, and reverse Bi-orthogonal of different degrees and orders for pre-processing of images.

  9. An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

    NASA Astrophysics Data System (ADS)

    Wang, Kun-Ching

    Traditional wavelet-based speech enhancement algorithms are ineffective in the presence of highly non-stationary noise because of the difficulties in the accurate estimation of the local noise spectrum. In this paper, a simple method of noise estimation employing the use of a voice activity detector is proposed. We can improve the output of a wavelet-based speech enhancement algorithm in the presence of random noise bursts according to the results of VAD decision. The noisy speech is first preprocessed using bark-scale wavelet packet decomposition (BSWPD) to convert a noisy signal into wavelet coefficients (WCs). It is found that the VAD using bark-scale spectral entropy, called as BS-Entropy, parameter is superior to other energy-based approach especially in variable noise-level. The wavelet coefficient threshold (WCT) of each subband is then temporally adjusted according to the result of VAD approach. In a speech-dominated frame, the speech is categorized into either a voiced frame or an unvoiced frame. A voiced frame possesses a strong tone-like spectrum in lower subbands, so that the WCs of lower-band must be reserved. On the contrary, the WCT tends to increase in lower-band if the speech is categorized as unvoiced. In a noise-dominated frame, the background noise can be almost completely removed by increasing the WCT. The objective and subjective experimental results are then used to evaluate the proposed system. The experiments show that this algorithm is valid on various noise conditions, especially for color noise and non-stationary noise conditions.

  10. Application of a Multidimensional Wavelet Denoising Algorithm for the Detection and Characterization of Astrophysical Sources of Gamma Rays

    SciTech Connect

    Digel, S.W.; Zhang, B.; Chiang, J.; Fadili, J.M.; Starck, J.-L.; /Saclay /Stanford U., Statistics Dept.

    2005-12-02

    Zhang, Fadili, & Starck have recently developed a denoising procedure for Poisson data that offers advantages over other methods of intensity estimation in multiple dimensions. Their procedure, which is nonparametric, is based on thresholding wavelet coefficients. The restoration algorithm applied after thresholding provides good conservation of source flux. We present an investigation of the procedure of Zhang et al. for the detection and characterization of astrophysical sources of high-energy gamma rays, using realistic simulated observations with the Large Area Telescope (LAT). The LAT is to be launched in late 2007 on the Gamma-ray Large Area Space Telescope mission. Source detection in the LAT data is complicated by the low fluxes of point sources relative to the diffuse celestial background, the limited angular resolution, and the tremendous variation of that resolution with energy (from tens of degrees at {approx}30 MeV to 0.1{sup o} at 10 GeV). The algorithm is very fast relative to traditional likelihood model fitting, and permits immediate estimation of spectral properties. Astrophysical sources of gamma rays, especially active galaxies, are typically quite variable, and our current work may lead to a reliable method to quickly characterize the flaring properties of newly-detected sources.

  11. Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

    NASA Astrophysics Data System (ADS)

    Mishra, C.; Samantaray, A. K.; Chakraborty, G.

    2016-05-01

    Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data1

  12. Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

    NASA Astrophysics Data System (ADS)

    Xu, Fan; Wang, Yuanqing

    2015-11-01

    Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.

  13. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

    NASA Astrophysics Data System (ADS)

    Abbasion, S.; Rafsanjani, A.; Farshidianfar, A.; Irani, N.

    2007-10-01

    Due to the importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The results that we achieved from wavelet analysis and SVM are fully in agreement with empirical result.

  14. Evaluation of Wavelet and Non-Local Mean Denoising of Terrestrial Laser Scanning Data for Small-Scale Joint Roughness Estimation

    NASA Astrophysics Data System (ADS)

    Bitenc, M.; Kieffer, D. S.; Khoshelham, K.

    2016-06-01

    Terrestrial Laser Scanning (TLS) is a well-known remote sensing tool that enables precise 3D acquisition of surface morphology from distances of a few meters to a few kilometres. The morphological representations obtained are important in engineering geology and rock mechanics, where surface morphology details are of particular interest in rock stability problems and engineering construction. The actual size of the discernible surface detail depends on the instrument range error (noise effect) and effective data resolution (smoothing effect). Range error can be (partly) removed by applying a denoising method. Based on the positive results from previous studies, two denoising methods, namely 2D wavelet transform (WT) and non-local mean (NLM), are tested here, with the goal of obtaining roughness estimations that are suitable in the context of rock engineering practice. Both methods are applied in two variants: conventional Discrete WT (DWT) and Stationary WT (SWT), classic NLM (NLM) and probabilistic NLM (PNLM). The noise effect and denoising performance are studied in relation to the TLS effective data resolution. Analyses are performed on the reference data acquired by a highly precise Advanced TOpometric Sensor (ATOS) on a 20x30 cm rock joint sample. Roughness ratio is computed by comparing the noisy and denoised surfaces to the original ATOS surface. The roughness ratio indicates the success of all denoising methods. Besides, it shows that SWT oversmoothes the surface and the performance of the DWT, NLM and PNLM vary with the noise level and data resolution. The noise effect becomes less prominent when data resolution decreases.

  15. Improving the performance of the prony method using a wavelet domain filter for MRI denoising.

    PubMed

    Jaramillo, Rodney; Lentini, Marianela; Paluszny, Marco

    2014-01-01

    The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method.

  16. Wavelet De-noising of GNSS Based Bridge Health Monitoring Data

    NASA Astrophysics Data System (ADS)

    Ogundipe, Oluropo; Lee, Jae Kang; Roberts, Gethin Wyn

    2014-11-01

    GNSS signal multipath occurs when the GNSS signal reflects of objects in the antenna environment and arrives at the antenna via multiple paths. A bridge environment is one that is prone to multipath with the bridge structure, as well as passing vehicles providing static and dynamic sources of multipath. In this paper, the Wavelet Transform (WT) is applied to bridge data collected on the Machang cable stayed bridge in Korea. The WT algorithm was applied to the GNSS derived bridge defection data at the mid-span. Up to 41% improvement in RMS was observed afterwavelet shrinkage de-noisingwas applied.Application of this algorithm to the torsion data showed significant improvement with the residual average and RMS decreased by 40% and 45% respectively. This method enabled the generation of more accurate information for bridge health monitoring systems in terms of the analysis of frequency, mode shape and three dimensional defections.

  17. Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising

    PubMed Central

    Lentini, Marianela; Paluszny, Marco

    2014-01-01

    The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T2 MR images, and the filter is applied to each image before using the variant of the Prony method. PMID:24834108

  18. Subsidence measurement and DSM extraction of IFSAR data using anisotropic diffusion and wavelet denoising filters

    NASA Astrophysics Data System (ADS)

    Sartor, Kenneth; Allen, Josef De Vaughn; Ganthier, Emile; Rahmes, Mark; Tenali, Gnana Bhaskar; Kozaitis, Samuel

    2008-04-01

    The most commonly used smoothing algorithms for complex data processing are low pass filters. Unfortunately, an undesired side effect of the aforementioned techniques is the blurring of scene discontinuities in the interferogram. For Digital Surface Map (DSM) extraction and subsidence measurement, the smoothing of the scene discontinuities can cause inaccuracy in the final product. Our goal is to perform spatially non-uniform smoothing to overcome the aforementioned disadvantages. We achieve this by using an Anisotropic Non-Linear Diffuser (ANDI). Here, in this paper we will show the utility of ANDI filtering on simulated and actual Interferometric Synthetic Aperture Radar (IFSAR) data for preprocessing, subsidence measurement and DSM extraction to overcome the difficulties of typical filters. We also compare the results of the ANDI filter with a wavelet filter. Finally, we detail some of our results of the New Orleans IFSAR research project with Canadian Space Agency, NASA, and USGS. The Harris LiteSite TM Urban 3D Modeling software is used to illustrate some of the results of our RADARSAT-1 processing.

  19. A de-noising algorithm based on wavelet threshold-exponential adaptive window width-fitting for ground electrical source airborne transient electromagnetic signal

    NASA Astrophysics Data System (ADS)

    Ji, Yanju; Li, Dongsheng; Yu, Mingmei; Wang, Yuan; Wu, Qiong; Lin, Jun

    2016-05-01

    The ground electrical source airborne transient electromagnetic system (GREATEM) on an unmanned aircraft enjoys considerable prospecting depth, lateral resolution and detection efficiency, etc. In recent years it has become an important technical means of rapid resources exploration. However, GREATEM data are extremely vulnerable to stationary white noise and non-stationary electromagnetic noise (sferics noise, aircraft engine noise and other human electromagnetic noises). These noises will cause degradation of the imaging quality for data interpretation. Based on the characteristics of the GREATEM data and major noises, we propose a de-noising algorithm utilizing wavelet threshold method and exponential adaptive window width-fitting. Firstly, the white noise is filtered in the measured data using the wavelet threshold method. Then, the data are segmented using data window whose step length is even logarithmic intervals. The data polluted by electromagnetic noise are identified within each window based on the discriminating principle of energy detection, and the attenuation characteristics of the data slope are extracted. Eventually, an exponential fitting algorithm is adopted to fit the attenuation curve of each window, and the data polluted by non-stationary electromagnetic noise are replaced with their fitting results. Thus the non-stationary electromagnetic noise can be effectively removed. The proposed algorithm is verified by the synthetic and real GREATEM signals. The results show that in GREATEM signal, stationary white noise and non-stationary electromagnetic noise can be effectively filtered using the wavelet threshold-exponential adaptive window width-fitting algorithm, which enhances the imaging quality.

  20. 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.

  1. Focal artifact removal from ongoing EEG--a hybrid approach based on spatially-constrained ICA and wavelet de-noising.

    PubMed

    Akhtar, Muhammad Tahir; James, Christopher J

    2009-01-01

    Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially-constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as all ICs are not identified in the usual manner due to the square mixing assumption. Simulation results demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.

  2. An 8.12 μW wavelet denoising chip for PPG detection and portable heart rate monitoring in 0.18 μm CMOS

    NASA Astrophysics Data System (ADS)

    Xiang, Li; Xu, Zhang; Peng, Li; Xiaohui, Hu; Hongda, Chen

    2016-05-01

    A low power wavelet denoising chip for photoplethysmography (PPG) detection and portable heart rate monitoring is presented. To eliminate noise and improve detection accuracy, Harr wavelet (HWT) is chosen as the processing tool. An optimized finite impulse response structure is proposed to lower the computational complexity of proposed algorithm, which is benefit for reducing the power consumption of proposed chip. The modulus maxima pair location module is design to accurately locate the PPG peaks. A clock control unit is designed to further reduce the power consumption of the proposed chip. Fabricated with the 0.18 μm N-well CMOS 1P6M technology, the power consumption of proposed chip is only 8.12 μW in 1 V voltage supply. Validated with PPG signals in multiparameter intelligent monitoring in intensive care databases and signals acquired by the wrist photoelectric volume detection front end, the proposed chip can accurately detect PPG signals. The average sensitivity and positive prediction are 99.91% and 100%, respectively.

  3. A Comprehensive Noise Robust Speech Parameterization Algorithm Using Wavelet Packet Decomposition-Based Denoising and Speech Feature Representation Techniques

    NASA Astrophysics Data System (ADS)

    Kotnik, Bojan; Kačič, Zdravko

    2007-12-01

    This paper concerns the problem of automatic speech recognition in noise-intense and adverse environments. The main goal of the proposed work is the definition, implementation, and evaluation of a novel noise robust speech signal parameterization algorithm. The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition. A new modified soft thresholding algorithm based on time-frequency adaptive threshold determination was developed to efficiently reduce the level of additive noise in the input noisy speech signal. A two-stage Gaussian mixture model (GMM)-based classifier was developed to perform speech/nonspeech as well as voiced/unvoiced classification. The adaptive topology of the wavelet packet decomposition tree based on voiced/unvoiced detection was introduced to separately analyze voiced and unvoiced segments of the speech signal. The main feature vector consists of a combination of log-root compressed wavelet packet parameters, and autoregressive parameters. The final output feature vector is produced using a two-staged feature vector postprocessing procedure. In the experimental framework, the noisy speech databases Aurora 2 and Aurora 3 were applied together with corresponding standardized acoustical model training/testing procedures. The automatic speech recognition performance achieved using the proposed noise robust speech parameterization procedure was compared to the standardized mel-frequency cepstral coefficient (MFCC) feature extraction procedures ETSI ES 201 108 and ETSI ES 202 050.

  4. Study on torpedo fuze signal denoising method based on WPT

    NASA Astrophysics Data System (ADS)

    Zhao, Jun; Sun, Changcun; Zhang, Tao; Ren, Zhiliang

    2013-07-01

    Torpedo fuze signal denoising is an important action to ensure reliable operation of fuze. Based on the good characteristics of wavelet packet transform (WPT) in signal denoising, the paper used wavelet packet transform to denoise the fuze signal under a complex background interference, and a simulation of the denoising results with Matlab is performed. Simulation result shows that the WPT denoising method can effectively eliminate background noise exist in torpedo fuze target signal with higher precision and less distortion, leading to advance the reliability of torpedo fuze operation.

  5. A New Adaptive Image Denoising Method Based on Neighboring Coefficients

    NASA Astrophysics Data System (ADS)

    Biswas, Mantosh; Om, Hari

    2016-03-01

    Many good techniques have been discussed for image denoising that include NeighShrink, improved adaptive wavelet denoising method based on neighboring coefficients (IAWDMBNC), improved wavelet shrinkage technique for image denoising (IWST), local adaptive wiener filter (LAWF), wavelet packet thresholding using median and wiener filters (WPTMWF), adaptive image denoising method based on thresholding (AIDMT). These techniques are based on local statistical description of the neighboring coefficients in a window. These methods however do not give good quality of the images since they cannot modify and remove too many small wavelet coefficients simultaneously due to the threshold. In this paper, a new image denoising method is proposed that shrinks the noisy coefficients using an adaptive threshold. Our method overcomes these drawbacks and it has better performance than the NeighShrink, IAWDMBNC, IWST, LAWF, WPTMWF, and AIDMT denoising methods.

  6. Denoising of high resolution small animal 3D PET data using the non-subsampled Haar wavelet transform

    NASA Astrophysics Data System (ADS)

    Ochoa Domínguez, Humberto de Jesús; Máynez, Leticia O.; Vergara Villegas, Osslan O.; Mederos, Boris; Mejía, José M.; Cruz Sánchez, Vianey G.

    2015-06-01

    PET allows functional imaging of the living tissue. However, one of the most serious technical problems affecting the reconstructed data is the noise, particularly in images of small animals. In this paper, a method for high-resolution small animal 3D PET data is proposed with the aim to reduce the noise and preserve details. The method is based on the estimation of the non-subsampled Haar wavelet coefficients by using a linear estimator. The procedure is applied to the volumetric images, reconstructed without correction factors (plane reconstruction). Results show that the method preserves the structures and drastically reduces the noise that contaminates the image.

  7. Research on denoising in WDM laser inter-satellites communication system

    NASA Astrophysics Data System (ADS)

    Wen, Chuanhua; Su, Yang; Li, Yuquan; Zhou, Li

    2006-09-01

    This paper proposes a method of wavelet analysis for de-noising at receiver system in WDM laser inter-satellites communication. Background noises such as galactic noise, sunlight and etc make the received power reduce. The noisy signal is decomposed using wavelets and wavelet packets; then is transformed into wavelet coefficients and the lower order coefficients are removed by applying a soft threshold. De-noised signal is obtained by reconstructing with the remaining coefficients. In this paper, we evaluate different wavelet analysis for de-noising at receiver system in inter-satellites laser communication. Simulation results indicate that if the wavelet de-noising method is used with different wavelet analyzing functions, it will improves the signal to noise ratio (SNR) about 2 dB when the signal frequency is 1.5 GHz.

  8. Multiresolution Bilateral Filtering for Image Denoising

    PubMed Central

    Zhang, Ming; Gunturk, Bahadir K.

    2008-01-01

    The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided. PMID:19004705

  9. An image denoising application using shearlets

    NASA Astrophysics Data System (ADS)

    Sevindir, Hulya Kodal; Yazici, Cuneyt

    2013-10-01

    Medical imaging is a multidisciplinary field related to computer science, electrical/electronic engineering, physics, mathematics and medicine. There has been dramatic increase in variety, availability and resolution of medical imaging devices for the last half century. For proper medical imaging highly trained technicians and clinicians are needed to pull out clinically pertinent information from medical data correctly. Artificial systems must be designed to analyze medical data sets either in a partially or even a fully automatic manner to fulfil the need. For this purpose there has been numerous ongoing research for finding optimal representations in image processing and computer vision [1, 18]. Medical images almost always contain artefacts and it is crucial to remove these artefacts to obtain healthy results. Out of many methods for denoising images, in this paper, two denoising methods, wavelets and shearlets, have been applied to mammography images. Comparing these two methods, shearlets give better results for denoising such data.

  10. Adaptive Fourier decomposition based ECG denoising.

    PubMed

    Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

    2016-10-01

    A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition.

  11. [DR image denoising based on Laplace-Impact mixture model].

    PubMed

    Feng, Guo-Dong; He, Xiang-Bin; Zhou, He-Qin

    2009-07-01

    A novel DR image denoising algorithm based on Laplace-Impact mixture model in dual-tree complex wavelet domain is proposed in this paper. It uses local variance to build probability density function of Laplace-Impact model fitted to the distribution of high-frequency subband coefficients well. Within Laplace-Impact framework, this paper describes a novel method for image denoising based on designing minimum mean squared error (MMSE) estimators, which relies on strong correlation between amplitudes of nearby coefficients. The experimental results show that the algorithm proposed in this paper outperforms several state-of-art denoising methods such as Bayes least squared Gaussian scale mixture and Laplace prior.

  12. Determination and Visualization of pH Values in Anaerobic Digestion of Water Hyacinth and Rice Straw Mixtures Using Hyperspectral Imaging with Wavelet Transform Denoising and Variable Selection

    PubMed Central

    Zhang, Chu; Ye, Hui; Liu, Fei; He, Yong; Kong, Wenwen; Sheng, Kuichuan

    2016-01-01

    Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874–1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algorithm (SPA), random frog (RF) and variable importance in projection (VIP) were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS) and a back propagation neural network (BPNN) were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp) of 0.911 and root mean square error of prediction (RMSEP) of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging. PMID:26901202

  13. Chemical Peels

    MedlinePlus

    ... the complications or potential side effects of a chemical peel? Temporary or permanent change in skin color, particularly for women on birth control pills, who subsequently become pregnant or have a history of brownish facial ... after having a chemical peel? All peels require some follow-up care: ...

  14. Iterated oversampled filter banks and wavelet frames

    NASA Astrophysics Data System (ADS)

    Selesnick, Ivan W.; Sendur, Levent

    2000-12-01

    This paper takes up the design of wavelet tight frames that are analogous to Daubechies orthonormal wavelets - that is, the design of minimal length wavelet filters satisfying certain polynomial properties, but now in the oversampled case. The oversampled dyadic DWT considered in this paper is based on a single scaling function and tow distinct wavelets. Having more wavelets than necessary gives a closer spacing between adjacent wavelets within the same scale. As a result, the transform is nearly shift-invariant, and can be used to improve denoising. Because the associated time- frequency lattice preserves the dyadic structure of the critically sampled DWT it can be used with tree-based denoising algorithms that exploit parent-child correlation.

  15. Study on De-noising Technology of Radar Life Signal

    NASA Astrophysics Data System (ADS)

    Yang, Xiu-Fang; Wang, Lian-Huan; Ma, Jiang-Fei; Wang, Pei-Pei

    2016-05-01

    Radar detection is a kind of novel life detection technology, which can be applied to medical monitoring, anti-terrorism and disaster relief street fighting, etc. As the radar life signal is very weak, it is often submerged in the noise. Because of non-stationary and randomness of these clutter signals, it is necessary to denoise efficiently before extracting and separating the useful signal. This paper improves the radar life signal's theoretical model of the continuous wave, does de-noising processing by introducing lifting wavelet transform and determine the best threshold function through comparing the de-noising effects of different threshold functions. The result indicates that both SNR and MSE of the signal are better than the traditional ones by introducing lifting wave transform and using a new improved soft threshold function de-noising method..

  16. Dermal peels.

    PubMed

    Coleman, W P

    2001-07-01

    Dermal chemical peeling is a very satisfying procedure for patients and physicians alike. Although not providing the ablation of deep wrinkles and scars that dermabrasion and laser procedures may accomplish, trichloroacetic acid peels usually result in few complications and rapid recovery. Patients can usually expect photographic improvement in their skin. The results are usually long lasting, and most patients do not need to repeat dermal peels for at least 2 years. Of all resurfacing procedures, dermal peeling provides the best benefit-to-risk ratio. PMID:11599397

  17. Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

    NASA Astrophysics Data System (ADS)

    Kittisuwan, Pichid

    2015-03-01

    The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

  18. A new performance evaluation scheme for jet engine vibration signal denoising

    NASA Astrophysics Data System (ADS)

    Sadooghi, Mohammad Saleh; Esmaeilzadeh Khadem, Siamak

    2016-08-01

    Denoising of a cargo-plane jet engine compressor vibration signal is investigated in this article. Discrete wavelet transform and two families of Donoho-Johnston and parameter method thresholding, are applied to vibration signal. Eighty four combinations of wavelet thresholding and mother wavelet are evaluated. A new performance evaluation scheme for optimal selection of mother wavelet and thresholding method combination is proposed in this paper, which is make a trade off between four performance criteria of signal to noise ratio, percentage root mean square difference, Cross-correlation, and mean square error. Dmeyer mother wavelet (dmey) combined with Rigorous SURE thresholding has the maximum trade off value and was selected as the most appropriate combination for denoising of the signal. It was shown that inappropriate combination leads to data losing. Also higher performance of proposed trade off with respect to other criteria was proven graphically.

  19. Chemical peeling.

    PubMed

    Forte, R; Hack, J; Jackson, I T

    1993-01-01

    This article explores the wide range of chemical facial peels, which include phenol, trichloroacetic acid, and alpha-hydroxy acids. The application of these substances will be described in addition to the contraindications to this type of treatment.

  20. Twofold processing for denoising ultrasound medical images.

    PubMed

    Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

    2015-01-01

    Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India. PMID:26697285

  1. A new study on mammographic image denoising using multiresolution techniques

    NASA Astrophysics Data System (ADS)

    Dong, Min; Guo, Ya-Nan; Ma, Yi-De; Ma, Yu-run; Lu, Xiang-yu; Wang, Ke-ju

    2015-12-01

    Mammography is the most simple and effective technology for early detection of breast cancer. However, the lesion areas of breast are difficult to detect which due to mammograms are mixed with noise. This work focuses on discussing various multiresolution denoising techniques which include the classical methods based on wavelet and contourlet; moreover the emerging multiresolution methods are also researched. In this work, a new denoising method based on dual tree contourlet transform (DCT) is proposed, the DCT possess the advantage of approximate shift invariant, directionality and anisotropy. The proposed denoising method is implemented on the mammogram, the experimental results show that the emerging multiresolution method succeeded in maintaining the edges and texture details; and it can obtain better performance than the other methods both on visual effects and in terms of the Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) values.

  2. Wavelets in medical imaging

    NASA Astrophysics Data System (ADS)

    Zahra, Noor e.; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H.

    2012-07-01

    The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

  3. Wavelets in medical imaging

    SciTech Connect

    Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H.

    2012-07-17

    The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

  4. Denoising in digital speckle pattern interferometry using wave atoms.

    PubMed

    Federico, Alejandro; Kaufmann, Guillermo H

    2007-05-15

    We present an effective method for speckle noise removal in digital speckle pattern interferometry, which is based on a wave-atom thresholding technique. Wave atoms are a variant of 2D wavelet packets with a parabolic scaling relation and improve the sparse representation of fringe patterns when compared with traditional expansions. The performance of the denoising method is analyzed by using computer-simulated fringes, and the results are compared with those produced by wavelet and curvelet thresholding techniques. An application of the proposed method to reduce speckle noise in experimental data is also presented.

  5. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

    PubMed Central

    Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin

    2015-01-01

    Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis. PMID:25619991

  6. Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Duan, Yabo; Song, Chengtian

    2016-10-01

    Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.

  7. Voxel-Wise Functional Connectomics Using Arterial Spin Labeling Functional Magnetic Resonance Imaging: The Role of Denoising.

    PubMed

    Liang, Xiaoyun; Connelly, Alan; Calamante, Fernando

    2015-11-01

    The objective of this study was to investigate voxel-wise functional connectomics using arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI). Since ASL signal has an intrinsically low signal-to-noise ratio (SNR), the role of denoising is evaluated; in particular, a novel denoising method, dual-tree complex wavelet transform (DT-CWT) combined with the nonlocal means (NLM) algorithm is implemented and evaluated. Simulations were conducted to evaluate the performance of the proposed method in denoising images and in detecting functional networks from noisy data (including the accuracy and sensitivity of detection). In addition, denoising was applied to in vivo ASL datasets, followed by network analysis using graph theoretical approaches. Efficiencies cost was used to evaluate the performance of denoising in detecting functional networks from in vivo ASL fMRI data. Simulations showed that denoising is effective in detecting voxel-wise functional networks from low SNR data and/or from data with small total number of time points. The capability of denoised voxel-wise functional connectivity analysis was also demonstrated with in vivo data. We concluded that denoising is important for voxel-wise functional connectivity using ASL fMRI and that the proposed DT-CWT-NLM method should be a useful ASL preprocessing step.

  8. The use of ensemble empirical mode decomposition as a novel denoising technique

    NASA Astrophysics Data System (ADS)

    Gaci, Said; Hachay, Olga; Zaourar, Naima

    2016-04-01

    Denoising is of a high importance in geophysical data processing. This paper suggests a new denoising technique based on the Ensemble Empirical mode decomposition (EEMD). This technique has been compared with the discrete wavelet transform (DWT) thresholding. Firstly, both methods have been implemented on synthetic signals with diverse waveforms ('blocks', 'heavy sine', 'Doppler', and 'mishmash'). The EEMD denoising method is proved to be the most efficient for 'blocks', 'heavy sine' and 'mishmash' signals for all the considered signal-to-noise ratio (SNR) values. However, the results obtained using the DWT thresholding are the most reliable for 'Doppler' signal, and the difference between the calculated mean square error (MSE) values using the studied methods is slight and decreases as the SNR value gets smaller values. Secondly, the denoising methods have been applied on real seismic traces recorded in the Algerian Sahara. It is shown that the proposed technique outperforms the DWT thresholding. In conclusion, the EEMD technique can provide a powerful tool for denoising seismic signals. Keywords: Ensemble Empirical mode decomposition (EEMD), Discrete wavelet transform (DWT), seismic signal.

  9. Multicomponent MR Image Denoising

    PubMed Central

    Manjón, José V.; Thacker, Neil A.; Lull, Juan J.; Garcia-Martí, Gracian; Martí-Bonmatí, Luís; Robles, Montserrat

    2009-01-01

    Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed. PMID:19888431

  10. Empirical Mode Decomposition Technique with Conditional Mutual Information for Denoising Operational Sensor Data

    SciTech Connect

    Omitaomu, Olufemi A; Protopopescu, Vladimir A; Ganguly, Auroop R

    2011-01-01

    A new approach is developed for denoising signals using the Empirical Mode Decomposition (EMD) technique and the Information-theoretic method. The EMD technique is applied to decompose a noisy sensor signal into the so-called intrinsic mode functions (IMFs). These functions are of the same length and in the same time domain as the original signal. Therefore, the EMD technique preserves varying frequency in time. Assuming the given signal is corrupted by high-frequency Gaussian noise implies that most of the noise should be captured by the first few modes. Therefore, our proposition is to separate the modes into high-frequency and low-frequency groups. We applied an information-theoretic method, namely mutual information, to determine the cut-off for separating the modes. A denoising procedure is applied only to the high-frequency group using a shrinkage approach. Upon denoising, this group is combined with the original low-frequency group to obtain the overall denoised signal. We illustrate our approach with simulated and real-world data sets. The results are compared to two popular denoising techniques in the literature, namely discrete Fourier transform (DFT) and discrete wavelet transform (DWT). We found that our approach performs better than DWT and DFT in most cases, and comparatively to DWT in some cases in terms of: (i) mean square error, (ii) recomputed signal-to-noise ratio, and (iii) visual quality of the denoised signals.

  11. Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise

    NASA Astrophysics Data System (ADS)

    Kishan, Harini; Seelamantula, Chandra Sekhar

    2015-09-01

    We propose optimal bilateral filtering techniques for Gaussian noise suppression in images. To achieve maximum denoising performance via optimal filter parameter selection, we adopt Stein's unbiased risk estimate (SURE)-an unbiased estimate of the mean-squared error (MSE). Unlike MSE, SURE is independent of the ground truth and can be used in practical scenarios where the ground truth is unavailable. In our recent work, we derived SURE expressions in the context of the bilateral filter and proposed SURE-optimal bilateral filter (SOBF). We selected the optimal parameters of SOBF using the SURE criterion. To further improve the denoising performance of SOBF, we propose variants of SOBF, namely, SURE-optimal multiresolution bilateral filter (SMBF), which involves optimal bilateral filtering in a wavelet framework, and SURE-optimal patch-based bilateral filter (SPBF), where the bilateral filter parameters are optimized on small image patches. Using SURE guarantees automated parameter selection. The multiresolution and localized denoising in SMBF and SPBF, respectively, yield superior denoising performance when compared with the globally optimal SOBF. Experimental validations and comparisons show that the proposed denoisers perform on par with some state-of-the-art denoising techniques.

  12. 3D Wavelet-Based Filter and Method

    DOEpatents

    Moss, William C.; Haase, Sebastian; Sedat, John W.

    2008-08-12

    A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.

  13. The use of wavelet filters for reducing noise in posterior fossa Computed Tomography images

    SciTech Connect

    Pita-Machado, Reinado; Perez-Diaz, Marlen Lorenzo-Ginori, Juan V. Bravo-Pino, Rolando

    2014-11-07

    Wavelet transform based de-noising like wavelet shrinkage, gives the good results in CT. This procedure affects very little the spatial resolution. Some applications are reconstruction methods, while others are a posteriori de-noising methods. De-noising after reconstruction is very difficult because the noise is non-stationary and has unknown distribution. Therefore, methods which work on the sinogram-space don’t have this problem, because they always work over a known noise distribution at this point. On the other hand, the posterior fossa in a head CT is a very complex region for physicians, because it is commonly affected by artifacts and noise which are not eliminated during the reconstruction procedure. This can leads to some false positive evaluations. The purpose of our present work is to compare different wavelet shrinkage de-noising filters to reduce noise, particularly in images of the posterior fossa within CT scans in the sinogram-space. This work describes an experimental search for the best wavelets, to reduce Poisson noise in Computed Tomography (CT) scans. Results showed that de-noising with wavelet filters improved the quality of posterior fossa region in terms of an increased CNR, without noticeable structural distortions.

  14. Comparison of de-noising techniques for FIRST images

    SciTech Connect

    Fodor, I K; Kamath, C

    2001-01-22

    Data obtained through scientific observations are often contaminated by noise and artifacts from various sources. As a result, a first step in mining these data is to isolate the signal of interest by minimizing the effects of the contaminations. Once the data has been cleaned or de-noised, data mining can proceed as usual. In this paper, we describe our work in denoising astronomical images from the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. We are mining this survey to detect radio-emitting galaxies with a bent-double morphology. This task is made difficult by the noise in the images caused by the processing of the sensor data. We compare three different approaches to de-noising: thresholding of wavelet coefficients advocated in the statistical community, traditional Altering methods used in the image processing community, and a simple thresholding scheme proposed by FIRST astronomers. While each approach has its merits and pitfalls, we found that for our purpose, the simple thresholding scheme worked relatively well for the FIRST dataset.

  15. [A De-Noising Algorithm for Fluorescence Detection Signal of Mineral Oil in Water by SWT].

    PubMed

    Wang, Yu-tian; Cheng, Peng-fei; Hou, Pei-guo; Yang, Zhe

    2015-05-01

    Fluorescence analysis is an important means of detecting mineral oil in water pollutants because of high sensitivity, selectivity, ease of design, etc. Noise generated from Photo detector will affect the sensitivity of fluorescence detection system, so the elimination of fluorescence signal noise has been a hot issue. For the fluorescence signal, due to the length increase of the branch set, it produces some boundary issues. The dbN wavelet family can flexibly balance the border issues, retain the useful signals and get. rid of noise, the de-noising effects of dbN families are compared, the db7 wavelet is chosen as the optimal wavelet. The noisy fluorescence signal is statically decomposed into 5 levels via db7 wavelet, and the thresholds are chosen adaptively based on the wavelet entropy theory. The pure fluorescence signal is obtained after the approximation coefficients and detail coefficients quantified by thresholds reconstructed. Compared with the DWT, the signal de-noised via SWT has the advantage of information integrity and time translation invariance.

  16. Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

    NASA Astrophysics Data System (ADS)

    Ragheb, Amr M.; Osman, Heba; Abbas, Alaa M.; Elkaffas, Saleh M.; El-Tobely, Tarek A.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; Abd El-Samie, Fathi E.

    2012-12-01

    To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity-hue-saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.

  17. A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis

    NASA Astrophysics Data System (ADS)

    Li, Huailiang; Tuo, Xianguo; Shi, Rui; Zhang, Jinzhao; Henderson, Mark Julian; Courtois, Jérémie; Yan, Minhao

    2016-05-01

    An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmented gamma scanning system with large samples in which high continuum levels caused by Compton scattering are routinely encountered. De-noising data from the gamma ray spectrum measured by segmented gamma scanning system with improved, shift-invariant and traditional wavelet transform algorithms were all evaluated. The improved wavelet transform method generated significantly enhanced performance of the figure of merit, the root mean square error, the peak area, and the sample attenuation correction in the segmented gamma scanning system assays. We also found that the gamma energy spectrum can be viewed as a low frequency signal as well as high frequency noise superposition by the spectrum analysis. Moreover, a smoothed spectrum can be appropriate for straightforward automated quantitative analysis.

  18. Removal of correlated noise by modeling the signal of interest in the wavelet domain.

    PubMed

    Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried

    2009-06-01

    Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the "signal of interest" and that is applicable to correlated noise. We combine the intrascale model with a hidden Markov tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.

  19. Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis

    NASA Astrophysics Data System (ADS)

    Qin, Yi; Xing, Jianfeng; Mao, Yongfang

    2016-08-01

    Aimed at solving the key problem in weak transient detection, the present study proposes a new transient feature extraction approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding. Firstly, a fast optimization algorithm based on the Shannon entropy is developed to obtain the optimized Morlet wavelet parameter. Compared to the existing Morlet wavelet parameter optimization algorithm, this algorithm has lower computation complexity. After performing the optimized Morlet wavelet transform on the analyzed signal, the kurtosis index is used to select the characteristic scales and obtain the corresponding wavelet coefficients. From the time-frequency distribution of the periodic impulsive signal, it is found that the transient signal can be reconstructed by the wavelet coefficients at several characteristic scales, rather than the wavelet coefficients at just one characteristic scale, so as to improve the accuracy of transient detection. Due to the noise influence on the characteristic wavelet coefficients, the adaptive soft-thresholding method is applied to denoise these coefficients. With the denoised wavelet coefficients, the transient signal can be reconstructed. The proposed method was applied to the analysis of two simulated signals, and the diagnosis of a rolling bearing fault and a gearbox fault. The superiority of the method over the fast kurtogram method was verified by the results of simulation analysis and real experiments. It is concluded that the proposed method is extremely suitable for extracting the periodic impulsive feature from strong background noise.

  20. A novel de-noising method for B ultrasound images

    NASA Astrophysics Data System (ADS)

    Tian, Da-Yong; Mo, Jia-qing; Yu, Yin-Feng; Lv, Xiao-Yi; Yu, Xiao; Jia, Zhen-Hong

    2015-12-01

    B ultrasound as a kind of ultrasonic imaging, which has become the indispensable diagnosis method in clinical medicine. However, the presence of speckle noise in ultrasound image greatly reduces the image quality and interferes with the accuracy of the diagnosis. Therefore, how to construct a method which can eliminate the speckle noise effectively, and at the same time keep the image details effectively is the research target of the current ultrasonic image de-noising. This paper is intended to remove the inherent speckle noise of B ultrasound image. The novel algorithm proposed is based on both wavelet transformation of B ultrasound images and data fusion of B ultrasound images, with a smaller mean squared error (MSE) and greater signal to noise ratio (SNR) compared with other algorithms. The results of this study can effectively remove speckle noise from B ultrasound images, and can well preserved the details and edge information which will produce better visual effects.

  1. A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction

    SciTech Connect

    Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frederic

    2015-02-15

    Purpose: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy–Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. Methods: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy–Richardson deconvolution algorithm to the current estimation of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Results: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. Conclusions: This study demonstrates the feasibility of incorporating the Lucy–Richardson deconvolution associated with a

  2. Translation invariant directional framelet transform combined with Gabor filters for image denoising.

    PubMed

    Shi, Yan; Yang, Xiaoyuan; Guo, Yuhua

    2014-01-01

    This paper is devoted to the study of a directional lifting transform for wavelet frames. A nonsubsampled lifting structure is developed to maintain the translation invariance as it is an important property in image denoising. Then, the directionality of the lifting-based tight frame is explicitly discussed, followed by a specific translation invariant directional framelet transform (TIDFT). The TIDFT has two framelets ψ1, ψ2 with vanishing moments of order two and one respectively, which are able to detect singularities in a given direction set. It provides an efficient and sparse representation for images containing rich textures along with properties of fast implementation and perfect reconstruction. In addition, an adaptive block-wise orientation estimation method based on Gabor filters is presented instead of the conventional minimization of residuals. Furthermore, the TIDFT is utilized to exploit the capability of image denoising, incorporating the MAP estimator for multivariate exponential distribution. Consequently, the TIDFT is able to eliminate the noise effectively while preserving the textures simultaneously. Experimental results show that the TIDFT outperforms some other frame-based denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.

  3. Lifting wavelet method of target detection

    NASA Astrophysics Data System (ADS)

    Han, Jun; Zhang, Chi; Jiang, Xu; Wang, Fang; Zhang, Jin

    2009-11-01

    Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.

  4. Sonar target enhancement by shrinkage of incoherent wavelet coefficients.

    PubMed

    Hunter, Alan J; van Vossen, Robbert

    2014-01-01

    Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.

  5. Chemistry with a Peel.

    ERIC Educational Resources Information Center

    Borer, Londa; Larsen, Eric

    1997-01-01

    Presents experiments that introduce natural product chemistry into high school classrooms. In the laboratory activities, students isolate and analyze the oil in orange peels. Students also perform a steam distillation and learn about terpenes. (DDR)

  6. Microdermabrasion with chemical peel.

    PubMed

    Kisner, A M

    2001-03-01

    Microdermabrasion is a popular, noninvasive superficial skin treatment. The author describes the benefits of microdermabrasion combined with a trichloroacetic acid peel to improve the appearance of moderately deep rhytids, acne scars, and photodamaged skin.

  7. Wavelet analysis deformation monitoring data of high-speed railway bridge

    NASA Astrophysics Data System (ADS)

    Tang, ShiHua; Huang, Qing; Zhou, Conglin; Xu, HongWei; Liu, YinTao; Li, FeiDa

    2015-12-01

    Deformation monitoring data of high-speed railway bridges will inevitably be affected because of noise pollution, A deformation monitoring point of high-speed railway bridge was measurd by using sokkia SDL30 electronic level for a long time,which got a large number of deformation monitoring data. Based on the characteristics of the deformation monitoring data of high-speed railway bridge, which contain lots of noise. Based on the MATLAB software platform, 120 groups of deformation monitoring data were applied to analysis of wavelet denoising.sym6,db6 wavelet basis function were selected to analyze and remove the noise.The original signal was broken into three layers wavelet,which contain high frequency coefficients and low frequency coefficients.However, high frequency coefficient have plenty of noise.Adaptive method of soft and hard threshold were used to handle in the high frequency coefficient.Then,high frequency coefficient that was removed much of noise combined with low frequency coefficient to reconstitute and obtain reconstruction wavelet signal.Root Mean Square Error (RMSE) and Signal-To-Noise Ratio (SNR) were regarded as evaluation index of denoising,The smaller the root mean square error and the greater signal-to-noise ratio indicate that them have a good effect in denoising. We can surely draw some conclusions in the experimental analysis:the db6 wavelet basis function has a good effect in wavelet denoising by using a adaptive soft threshold method,which root mean square error is minimum and signal-to-noise ratio is maximum.Moreover,the reconstructed image are more smooth than original signal denoising after wavelet denoising, which removed noise and useful signal are obtained in the original signal.Compared to the other three methods, this method has a good effect in denoising, which not only retain useful signal in the original signal, but aiso reach the goal of removing noise. So, it has a strong practical value in a actual deformation monitoring

  8. Orthogonal wavelet moments and their multifractal invariants

    NASA Astrophysics Data System (ADS)

    Uchaev, Dm. V.; Uchaev, D. V.; Malinnikov, V. A.

    2015-02-01

    This paper introduces a new family of moments, namely orthogonal wavelet moments (OWMs), which are orthogonal realization of wavelet moments (WMs). In contrast to WMs with nonorthogonal kernel function, these moments can be used for multiresolution image representation and image reconstruction. The paper also introduces multifractal invariants (MIs) of OWMs which can be used instead of OWMs. Some reconstruction tests performed with noise-free and noisy images demonstrate that MIs of OWMs can also be used for image smoothing, sharpening and denoising. It is established that the reconstruction quality for MIs of OWMs can be better than corresponding orthogonal moments (OMs) and reduces to the reconstruction quality for the OMs if we use the zero scale level.

  9. Noise reduction of FBG sensor signal by using a wavelet transform

    NASA Astrophysics Data System (ADS)

    Cho, Yo-Han; Song, Minho

    2011-05-01

    We constructed a FBG (fiber Bragg grating) sensor system based on a fiber-optic Sagnac interferometer. A fiber-optic laser source is used as a strong light source to attain high signal-to-noise ratio. However the unstable output power and coherence noises of the fiber laser made it hard to separate the FBG signals from the interference signals of the fiber coils. To reduce noises and extract FBG sensor signals, we used a Gaussian curve-fitting and a wavelet transform. The wavelet transform is a useful tool for analyzing and denoising output signals. The feasibility of the wavelet transform denoising process is presented with the preliminary experimental results, which showed much better accuracy than the case with only the Gaussian curve-fitting algorithm.

  10. ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Kaur, Inderbir; Rajni, Rajni; Marwaha, Anupma

    2016-06-01

    Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.

  11. Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation

    NASA Astrophysics Data System (ADS)

    Lei, Sheau-Fang; Tung, Ying-Kai

    Spectral subtraction is commonly used for speech enhancement in a single channel system because of the simplicity of its implementation. However, this algorithm introduces perceptually musical noise while suppressing the background noise. We propose a wavelet-based approach in this paper for suppressing the background noise for speech enhancement in a single channel system. The wavelet packet transform, which emulates the human auditory system, is used to decompose the noisy signal into critical bands. Wavelet thresholding is then temporally adjusted with the noise power by time-adapted noise estimation. The proposed algorithm can efficiently suppress the noise while reducing speech distortion. Experimental results, including several objective measurements, show that the proposed wavelet-based algorithm outperforms spectral subtraction and other wavelet-based denoising approaches for speech enhancement for nonstationary noise environments.

  12. Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function.

    PubMed

    Lahmiri, Salim

    2016-03-01

    Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications. PMID:27222723

  13. OPTICAL COHERENCE TOMOGRAPHY HEART TUBE IMAGE DENOISING BASED ON CONTOURLET TRANSFORM.

    PubMed

    Guo, Qing; Sun, Shuifa; Dong, Fangmin; Gao, Bruce Z; Wang, Rui

    2012-01-01

    Optical Coherence Tomography(OCT) gradually becomes a very important imaging technology in the Biomedical field for its noninvasive, nondestructive and real-time properties. However, the interpretation and application of the OCT images are limited by the ubiquitous noise. In this paper, a denoising algorithm based on contourlet transform for the OCT heart tube image is proposed. A bivariate function is constructed to model the joint probability density function (pdf) of the coefficient and its cousin in contourlet domain. A bivariate shrinkage function is deduced to denoise the image by the maximum a posteriori (MAP) estimation. Three metrics, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and equivalent number of look (ENL), are used to evaluate the denoised image using the proposed algorithm. The results show that the signal-to-noise ratio is improved while the edges of object are preserved by the proposed algorithm. Systemic comparisons with other conventional algorithms, such as mean filter, median filter, RKT filter, Lee filter, as well as bivariate shrinkage function for wavelet-based algorithm are conducted. The advantage of the proposed algorithm over these methods is illustrated. PMID:25364626

  14. Iterative Regularization Denoising Method Based on OSV Model for BioMedical Image Denoising

    NASA Astrophysics Data System (ADS)

    Guan-nan, Chen; Rong, Chen; Zu-fang, Huang; Ju-qiang, Lin; Shang-yuan, Feng; Yong-zeng, Li; Zhong-jian, Teng

    2011-01-01

    Biomedical image denoising algorithm based on gradient dependent energy functional often compromised the biomedical image features like textures or certain details. This paper proposes an iterative regularization denoising method based on OSV model for biomedical image denoising. By using iterative regularization, the oscillating patterns of texture and detail are added back to fit and compute the original OSV model,and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed algorithm also be proved the convergence property. Experimental results show that the proposed method can achieve a batter result in preserving not only the features of textures for biomedical image denoising but also the details for biomedical image.

  15. Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Wan, Zhiguo; Pan, Jun; Zi, Yanyang; Wang, Yu; Chen, Binqiang; Sun, Hailiang; Yuan, Jing; He, Zhengjia

    2016-02-01

    Fault identification timely of rolling mill drivetrain is significant for guaranteeing product quality and realizing long-term safe operation. So, condition monitoring system of rolling mill drivetrain is designed and developed. However, because compound fault and weak fault feature information is usually sub-merged in heavy background noise, this task still faces challenge. This paper provides a possibility for fault identification of rolling mills drivetrain by proposing customized maximal-overlap multiwavelet denoising method. The effectiveness of wavelet denoising method mainly relies on the appropriate selections of wavelet base, transform strategy and threshold rule. First, in order to realize exact matching and accurate detection of fault feature, customized multiwavelet basis function is constructed via symmetric lifting scheme and then vibration signal is processed by maximal-overlap multiwavelet transform. Next, based on spatial dependency of multiwavelet transform coefficients, spatial neighboring coefficient data-driven group threshold shrinkage strategy is developed for denoising process by choosing the optimal group length and threshold via the minimum of Stein's Unbiased Risk Estimate. The effectiveness of proposed method is first demonstrated through compound fault identification of reduction gearbox on rolling mill. Then it is applied for weak fault identification of dedusting fan bearing on rolling mill and the results support its feasibility.

  16. Translation-invariant multiwavelet denoising using improved neighbouring coefficients and its application on rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Hailiang, Sun; Yanyang, Zi; Zhengjia, He; Xiaodong, Wang; Jing, Yuan

    2011-07-01

    The deficiencies of conventional neighbouring coefficients denoising are the invariant neighbouring window size and the global threshold; therefore, it cannot accurately represent local concentrated energy of the collected signals in engineering application. The improved neighbouring coefficients named Neighbouring Coefficients Dependent on Level (NCDL) is proposed. The size of neighbouring window varies with different decomposition levels and the threshold is chosen according to the neighbourhood. Translation invariant method can effectively weaken some visual artifacts, for example Gibbs phenomena in the neighbourhood of discontinuities. Multiwavelets have two or more scaling and wavelet functions. Compared with scalar wavelet, multiwavelets offer several excellent properties such as symmetric, orthogonal, compactly support and higher order of vanishing moment. A novel denoising method - translation invariant multiwavelet denoising with improved neighbouring coefficients is presented. The simulation signal proves the validity of the presented method. This method is then applied to the fault diagnosis of a locomotive rolling bearing. The results show that the present method can effectively extract the fault characteristic frequency of a slight scrape on the outer race of the rolling bearing.

  17. Discrete shearlet transform on GPU with applications in anomaly detection and denoising

    NASA Astrophysics Data System (ADS)

    Gibert, Xavier; Patel, Vishal M.; Labate, Demetrio; Chellappa, Rama

    2014-12-01

    Shearlets have emerged in recent years as one of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localized functions defined not only over a range of scales and locations, but also over a range of orientations and with highly anisotropic supports. As a result, shearlets are much more effective than traditional wavelets in handling the geometry of multidimensional data, and this was exploited in a wide range of applications from image and signal processing. However, despite their desirable properties, the wider applicability of shearlets is limited by the computational complexity of current software implementations. For example, denoising a single 512 × 512 image using a current implementation of the shearlet-based shrinkage algorithm can take between 10 s and 2 min, depending on the number of CPU cores, and much longer processing times are required for video denoising. On the other hand, due to the parallel nature of the shearlet transform, it is possible to use graphics processing units (GPU) to accelerate its implementation. In this paper, we present an open source stand-alone implementation of the 2D discrete shearlet transform using CUDA C++ as well as GPU-accelerated MATLAB implementations of the 2D and 3D shearlet transforms. We have instrumented the code so that we can analyze the running time of each kernel under different GPU hardware. In addition to denoising, we describe a novel application of shearlets for detecting anomalies in textured images. In this application, computation times can be reduced by a factor of 50 or more, compared to multicore CPU implementations.

  18. Geodesic denoising for optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Shahrian Varnousfaderani, Ehsan; Vogl, Wolf-Dieter; Wu, Jing; Gerendas, Bianca S.; Simader, Christian; Langs, Georg; Waldstein, Sebastian M.; Schmidt-Erfurth, Ursula

    2016-03-01

    Optical coherence tomography (OCT) is an optical signal acquisition method capturing micrometer resolution, cross-sectional three-dimensional images. OCT images are used widely in ophthalmology to diagnose and monitor retinal diseases such as age-related macular degeneration (AMD) and Glaucoma. While OCT allows the visualization of retinal structures such as vessels and retinal layers, image quality and contrast is reduced by speckle noise, obfuscating small, low intensity structures and structural boundaries. Existing denoising methods for OCT images may remove clinically significant image features such as texture and boundaries of anomalies. In this paper, we propose a novel patch based denoising method, Geodesic Denoising. The method reduces noise in OCT images while preserving clinically significant, although small, pathological structures, such as fluid-filled cysts in diseased retinas. Our method selects optimal image patch distribution representations based on geodesic patch similarity to noisy samples. Patch distributions are then randomly sampled to build a set of best matching candidates for every noisy sample, and the denoised value is computed based on a geodesic weighted average of the best candidate samples. Our method is evaluated qualitatively on real pathological OCT scans and quantitatively on a proposed set of ground truth, noise free synthetic OCT scans with artificially added noise and pathologies. Experimental results show that performance of our method is comparable with state of the art denoising methods while outperforming them in preserving the critical clinically relevant structures.

  19. Computed tomography perfusion imaging denoising using Gaussian process regression

    NASA Astrophysics Data System (ADS)

    Zhu, Fan; Carpenter, Trevor; Rodriguez Gonzalez, David; Atkinson, Malcolm; Wardlaw, Joanna

    2012-06-01

    Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.

  20. Denoising of Ultrasound Cervix Image Using Improved Anisotropic Diffusion Filter

    PubMed Central

    Rose, R Jemila; Allwin, S

    2015-01-01

    ABSTRACT Objective: The purpose of this study was to evaluate an improved oriented speckle reducing anisotropic diffusion (IADF) filter that suppress the speckle noise from ultrasound B-mode images and shows better result than previous filters such as anisotropic diffusion, wavelet denoising and local statistics. Methods: The clinical ultrasound images of the cervix were obtained by ATL HDI 5000 ultrasound machine from the Regional Cancer Centre, Medical College campus, Thiruvananthapuram. The standardized ways of organizing and storing the image were in the format of bmp and the dimensions of 256 × 256 with the help of an improved oriented speckle reducing anisotropic diffusion filter. For analysis, 24 ultrasound cervix images were tested and the performance measured. Results: This provides quality metrics in the case of maximum peak signal-to-noise ratio (PSNR) of 31 dB, structural similarity index map (SSIM) of 0.88 and edge preservation accuracy of 88%. Conclusion: The IADF filter is the optimal method and it is capable of strong speckle suppression with less computational complexity. PMID:26624591

  1. Magnetic resonance image denoising using multiple filters

    NASA Astrophysics Data System (ADS)

    Ai, Danni; Wang, Jinjuan; Miwa, Yuichi

    2013-07-01

    We introduced and compared ten denoisingfilters which are all proposed during last fifteen years. Especially, the state-of-art denoisingalgorithms, NLM and BM3D, have attracted much attention. Several expansions are proposed to improve the noise reduction based on these two algorithms. On the other hand, optimal dictionaries, sparse representations and appropriate shapes of the transform's support are also considered for the image denoising. The comparison among variousfiltersis implemented by measuring the SNR of a phantom image and denoising effectiveness of a clinical image. The computational time is finally evaluated.

  2. Analysis the application of several denoising algorithm in the astronomical image denoising

    NASA Astrophysics Data System (ADS)

    Jiang, Chao; Geng, Ze-xun; Bao, Yong-qiang; Wei, Xiao-feng; Pan, Ying-feng

    2014-02-01

    Image denoising is an important method of preprocessing, it is one of the forelands in the field of Computer Graphic and Computer Vision. Astronomical target imaging are most vulnerable to atmospheric turbulence and noise interference, in order to reconstruct the high quality image of the target, we need to restore the high frequency signal of image, but noise also belongs to the high frequency signal, so there will be noise amplification in the reconstruction process. In order to avoid this phenomenon, join image denoising in the process of reconstruction is a feasible solution. This paper mainly research on the principle of four classic denoising algorithm, which are TV, BLS - GSM, NLM and BM3D, we use simulate data for image denoising to analysis the performance of the four algorithms, experiments demonstrate that the four algorithms can remove the noise, the BM3D algorithm not only have high quality of denosing, but also have the highest efficiency at the same time.

  3. [Denoising and assessing method of additive noise in the ultraviolet spectrum of SO2 in flue gas].

    PubMed

    Zhou, Tao; Sun, Chang-Ku; Liu, Bin; Zhao, Yu-Mei

    2009-11-01

    The problem of denoising and assessing method of the spectrum of SO2 in flue gas was studied based on DOAS. The denoising procedure of the additive noise in the spectrum was divided into two parts: reducing the additive noise and enhancing the useful signal. When obtaining the absorption feature of measured gas, a multi-resolution preprocessing method of original spectrum was adopted for denoising by DWT (discrete wavelet transform). The signal energy operators in different scales were used to choose the denoising threshold and separate the useful signal from the noise. On the other hand, because there was no sudden change in the spectra of flue gas in time series, the useful signal component was enhanced according to the signal time dependence. And the standard absorption cross section was used to build the ideal absorption spectrum with the measured gas temperature and pressure. This ideal spectrum was used as the desired signal instead of the original spectrum in the assessing method to modify the SNR (signal-noise ratio). There were two different environments to do the proof test-in the lab and at the scene. In the lab, SO2 was measured several times with the system using this method mentioned above. The average deviation was less than 1.5%, while the repeatability was less than 1%. And the short range experiment data were better than the large range. In the scene of a power plant whose concentration of flue gas had a large variation range, the maximum deviation of this method was 2.31% in the 18 groups of contrast data. The experimental results show that the denoising effect of the scene spectrum was better than that of the lab spectrum. This means that this method can improve the SNR of the spectrum effectively, which is seriously polluted by additive noise. PMID:20101989

  4. Predicting apple tree leaf nitrogen content based on hyperspectral applying wavelet and wavelet packet analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Yao; Zheng, Lihua; Li, Minzan; Deng, Xiaolei; Sun, Hong

    2012-11-01

    The visible and NIR spectral reflectance were measured for apple leaves by using a spectrophotometer in fruit-bearing, fruit-falling and fruit-maturing period respectively, and the nitrogen content of each sample was measured in the lab. The analysis of correlation between nitrogen content of apple tree leaves and their hyperspectral data was conducted. Then the low frequency signal and high frequency noise reduction signal were extracted by using wavelet packet decomposition algorithm. At the same time, the original spectral reflectance was denoised taking advantage of the wavelet filtering technology. And then the principal components spectra were collected after PCA (Principal Component Analysis). It was known that the model built based on noise reduction principal components spectra reached higher accuracy than the other three ones in fruit-bearing period and physiological fruit-maturing period. Their calibration R2 reached 0.9529 and 0.9501, and validation R2 reached 0.7285 and 0.7303 respectively. While in the fruit-falling period the model based on low frequency principal components spectra reached the highest accuracy, and its calibration R2 reached 0.9921 and validation R2 reached 0.6234. The results showed that it was an effective way to improve ability of predicting apple tree nitrogen content based on hyperspectral analysis by using wavelet packet algorithm.

  5. Sonar target enhancement by shrinkage of incoherent wavelet coefficients.

    PubMed

    Hunter, Alan J; van Vossen, Robbert

    2014-01-01

    Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection. PMID:24437766

  6. Medium-depth chemical peels.

    PubMed

    Monheit, G D

    2001-07-01

    The combination medium-depth chemical peel (Jessner's solution +35% TCA) has been accepted as a safe, reliable, and effective method for the treatment of moderate photoaging skin. This article discusses the procedure in detail, including postoperative considerations. PMID:11599398

  7. The Hilbert-Huang Transform-Based Denoising Method for the TEM Response of a PRBS Source Signal

    NASA Astrophysics Data System (ADS)

    Hai, Li; Guo-qiang, Xue; Pan, Zhao; Hua-sen, Zhong; Khan, Muhammad Younis

    2016-08-01

    The denoising process is critical in processing transient electromagnetic (TEM) sounding data. For the full waveform pseudo-random binary sequences (PRBS) response, an inadequate noise estimation may result in an erroneous interpretation. We consider the Hilbert-Huang transform (HHT) and its application to suppress the noise in the PRBS response. The focus is on the thresholding scheme to suppress the noise and the analysis of the signal based on its Hilbert time-frequency representation. The method first decomposes the signal into the intrinsic mode function, and then, inspired by the thresholding scheme in wavelet analysis; an adaptive and interval thresholding is conducted to set to zero all the components in intrinsic mode function which are lower than a threshold related to the noise level. The algorithm is based on the characteristic of the PRBS response. The HHT-based denoising scheme is tested on the synthetic and field data with the different noise levels. The result shows that the proposed method has a good capability in denoising and detail preservation.

  8. Performance comparison of denoising filters for source camera identification

    NASA Astrophysics Data System (ADS)

    Cortiana, A.; Conotter, V.; Boato, G.; De Natale, F. G. B.

    2011-02-01

    Source identification for digital content is one of the main branches of digital image forensics. It relies on the extraction of the photo-response non-uniformity (PRNU) noise as a unique intrinsic fingerprint that efficiently characterizes the digital device which generated the content. Such noise is estimated as the difference between the content and its de-noised version obtained via denoising filter processing. This paper proposes a performance comparison of different denoising filters for source identification purposes. In particular, results achieved with a sophisticated 3D filter are presented and discussed with respect to state-of-the-art denoising filters previously employed in such a context.

  9. Chemical peeling in ethnic/dark skin.

    PubMed

    Roberts, Wendy E

    2004-01-01

    Chemical peeling for skin of color arose in ancient Egypt, Mesopotamia, and other ancient cultures in and around Africa. Our current fund of medical knowledge regarding chemical peeling is a result of centuries of experience and research. The list of agents for chemical peeling is extensive. In ethnic skin, our efforts are focused on superficial and medium-depth peeling agents and techniques. Indications for chemical peeling in darker skin include acne vulgaris, postinflammatory hyperpigmentation, melasma, scarring, photodamage, and pseudofolliculitis barbae. Careful selection of patients for chemical peeling should involve not only identification of Fitzpatrick skin type, but also determining ethnicity. Different ethnicities may respond unpredictably to chemical peeling regardless of skin phenotype. Familiarity with the properties each peeling agent used is critical. New techniques discussed for chemical peeling include spot peeling for postinflammatory hyperpigmentation and combination peels for acne and photodamage. Single- or combination-agent chemical peels are shown to be efficacious and safe. In conclusion, chemical peeling is a treatment of choice for numerous pigmentary and scarring disorders arising in dark skin tones. Familiarity with new peeling agents and techniques will lead to successful outcomes. PMID:15113287

  10. Local denoising of digital speckle pattern interferometry fringes by multiplicative correlation and weighted smoothing splines.

    PubMed

    Federico, Alejandro; Kaufmann, Guillermo H

    2005-05-10

    We evaluate the use of smoothing splines with a weighted roughness measure for local denoising of the correlation fringes produced in digital speckle pattern interferometry. In particular, we also evaluate the performance of the multiplicative correlation operation between two speckle patterns that is proposed as an alternative procedure to generate the correlation fringes. It is shown that the application of a normalization algorithm to the smoothed correlation fringes reduces the excessive bias generated in the previous filtering stage. The evaluation is carried out by use of computer-simulated fringes that are generated for different average speckle sizes and intensities of the reference beam, including decorrelation effects. A comparison with filtering methods based on the continuous wavelet transform is also presented. Finally, the performance of the smoothing method in processing experimental data is illustrated.

  11. Nonlocal Markovian models for image denoising

    NASA Astrophysics Data System (ADS)

    Salvadeo, Denis H. P.; Mascarenhas, Nelson D. A.; Levada, Alexandre L. M.

    2016-01-01

    Currently, the state-of-the art methods for image denoising are patch-based approaches. Redundant information present in nonlocal regions (patches) of the image is considered for better image modeling, resulting in an improved quality of filtering. In this respect, nonlocal Markov random field (MRF) models are proposed by redefining the energy functions of classical MRF models to adopt a nonlocal approach. With the new energy functions, the pairwise pixel interaction is weighted according to the similarities between the patches corresponding to each pair. Also, a maximum pseudolikelihood estimation of the spatial dependency parameter (β) for these models is presented here. For evaluating this proposal, these models are used as an a priori model in a maximum a posteriori estimation to denoise additive white Gaussian noise in images. Finally, results display a notable improvement in both quantitative and qualitative terms in comparison with the local MRFs.

  12. Adaptive Image Denoising by Mixture Adaptation

    NASA Astrophysics Data System (ADS)

    Luo, Enming; Chan, Stanley H.; Nguyen, Truong Q.

    2016-10-01

    We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad-hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper: First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. Experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms.

  13. A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.

    PubMed

    Gur, M Berke; Niezrecki, Christopher

    2011-04-01

    Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation.

  14. Efficient fourier-wavelet super-resolution.

    PubMed

    Robinson, M Dirk; Toth, Cynthia A; Lo, Joseph Y; Farsiu, Sina

    2010-10-01

    Super-resolution (SR) is the process of combining multiple aliased low-quality images to produce a high-resolution high-quality image. Aside from registration and fusion of low-resolution images, a key process in SR is the restoration and denoising of the fused images. We present a novel extension of the combined Fourier-wavelet deconvolution and denoising algorithm ForWarD to the multiframe SR application. Our method first uses a fast Fourier-base multiframe image restoration to produce a sharp, yet noisy estimate of the high-resolution image. Our method then applies a space-variant nonlinear wavelet thresholding that addresses the nonstationarity inherent in resolution-enhanced fused images. We describe a computationally efficient method for implementing this space-variant processing that leverages the efficiency of the fast Fourier transform (FFT) to minimize complexity. Finally, we demonstrate the effectiveness of this algorithm for regular imagery as well as in digital mammography. PMID:20460208

  15. A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.

    PubMed

    Gur, M Berke; Niezrecki, Christopher

    2011-04-01

    Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661

  16. Identification of formation interfaces by using wavelet and Fourier transforms

    NASA Astrophysics Data System (ADS)

    Mukherjee, Bappa; Srivardhan, V.; Roy, P. N. S.

    2016-05-01

    The identification of formation interfaces is of prime importance from well log data. The interfaces are not clearly discernible due to the presence of high and low frequency noise in the log response. Accurate bed boundary information is very crucial in hydrocarbon exploration and the problem has received considerable attention and many techniques have been proposed. Frequency spectrum based filtering techniques aids us in interpretation, but usually leads to inaccurate amplification of unwanted components of the log response. Wavelet transform is very effective in denoising the log response and can be carried out to filter low and high frequency components of signal. The use of Fourier and Wavelet transform in denoising the log data for obtaining formation interfaces is demonstrated in this work. The feasibility of the proposed technique is tested so that it can be used in the industry to decipher formation interfaces. The work flow is demonstrated by testing on wells belonging to the Upper Assam Basin, which are self-potential, gamma ray, and resistivity log responses.

  17. Speckle filtering of medical ultrasonic images using wavelet and guided filter.

    PubMed

    Zhang, Ju; Lin, Guangkuo; Wu, Lili; Cheng, Yun

    2016-02-01

    Speckle noise is an inherent yet ineffectual residual artifact in medical ultrasound images, which significantly degrades quality and restricts accuracy in automatic diagnostic techniques. Speckle reduction is therefore an important step prior to the analysis and processing of the ultrasound images. A new de-noising method based on an improved wavelet filter and guided filter is proposed in this paper. According to the characteristics of medical ultrasound images in the wavelet domain, an improved threshold function based on the universal wavelet threshold function is developed. The wavelet coefficients of speckle noise and noise-free signal are modeled as Rayleigh distribution and generalized Gaussian distribution respectively. The Bayesian maximum a posteriori estimation is applied to obtain a new wavelet shrinkage algorithm. The coefficients of the low frequency sub-band in the wavelet domain are filtered by guided filter. The filtered image is then obtained by using the inverse wavelet transformation. Experiments with the comparison of the other seven de-speckling filters are conducted. The results show that the proposed method not only has a strong de-speckling ability, but also keeps the image details, such as the edge of a lesion. PMID:26489484

  18. Infrared image denoising by nonlocal means filtering

    NASA Astrophysics Data System (ADS)

    Dee-Noor, Barak; Stern, Adrian; Yitzhaky, Yitzhak; Kopeika, Natan

    2012-05-01

    The recently introduced non-local means (NLM) image denoising technique broke the traditional paradigm according to which image pixels are processed by their surroundings. Non-local means technique was demonstrated to outperform state-of-the art denoising techniques when applied to images in the visible. This technique is even more powerful when applied to low contrast images, which makes it tractable for denoising infrared (IR) images. In this work we investigate the performance of NLM applied to infrared images. We also present a new technique designed to speed-up the NLM filtering process. The main drawback of the NLM is the large computational time required by the process of searching similar patches. Several techniques were developed during the last years to reduce the computational burden. Here we present a new techniques designed to reduce computational cost and sustain optimal filtering results of NLM technique. We show that the new technique, which we call Multi-Resolution Search NLM (MRS-NLM), reduces significantly the computational cost of the filtering process and we present a study of its performance on IR images.

  19. Glycolic acid peel therapy - a current review.

    PubMed

    Sharad, Jaishree

    2013-01-01

    Chemical peels have been time-tested and are here to stay. Alpha-hydroxy peels are highly popular in the dermatologist's arsenal of procedures. Glycolic acid peel is the most common alpha-hydroxy acid peel, also known as fruit peel. It is simple, inexpensive, and has no downtime. This review talks about various studies of glycolic acid peels for various indications, such as acne, acne scars, melasma, postinflammatory hyperpigmentation, photoaging, and seborrhea. Combination therapies and treatment procedure are also discussed. Careful review of medical history, examination of the skin, and pre-peel priming of skin are important before every peel. Proper patient selection, peel timing, and neutralization on-time will ensure good results, with no side effects. Depth of the glycolic acid peel depends on the concentration of the acid used, the number of coats applied, and the time for which it is applied. Hence, it can be used as a very superficial peel, or even a medium depth peel. It has been found to be very safe with Fitzpatrick skin types I-IV. All in all, it is a peel that is here to stay. PMID:24399880

  20. Lidar signal de-noising by singular value decomposition

    NASA Astrophysics Data System (ADS)

    Wang, Huanxue; Liu, Jianguo; Zhang, Tianshu

    2014-11-01

    Signal de-noising remains an important problem in lidar signal processing. This paper presents a de-noising method based on singular value decomposition. Experimental results on lidar simulated signal and real signal show that the proposed algorithm not only improves the signal-to-noise ratio effectively, but also preserves more detail information.

  1. Evaluation of optical properties for real photonic crystal fiber based on total variation in wavelet domain

    NASA Astrophysics Data System (ADS)

    Shen, Yan; Wang, Xin; Lou, Shuqin; Lian, Zhenggang; Zhao, Tongtong

    2016-09-01

    An evaluation method based on the total variation model (TV) in wavelet domain is proposed for modeling optical properties of real photonic crystal fibers (PCFs). The TV model in wavelet domain is set up to suppress the noise of the original image effectively and rebuild the cross section images of real PCFs with high accuracy. The optical properties of three PCFs are evaluated, including two kinds of PCFs that supplied from the Crystal Fiber A/S and a homemade side-leakage PCF, by using the combination of the proposed model and finite element method. Numerical results demonstrate that the proposed method can obtain high noise suppression ratio and effectively reduce the noise of cross section images of PCFs, which leads to an accurate evaluation of optical properties of real PCFs. To the best of our knowledge, it is the first time to denoise the cross section images of PCFs with the TV model in the wavelet domain.

  2. CW-THz image contrast enhancement using wavelet transform and Retinex

    NASA Astrophysics Data System (ADS)

    Chen, Lin; Zhang, Min; Hu, Qi-fan; Huang, Ying-Xue; Liang, Hua-Wei

    2015-10-01

    To enhance continuous wave terahertz (CW-THz) scanning images contrast and denoising, a method based on wavelet transform and Retinex theory was proposed. In this paper, the factors affecting the quality of CW-THz images were analysed. Second, an approach of combination of the discrete wavelet transform (DWT) and a designed nonlinear function in wavelet domain for the purpose of contrast enhancing was applied. Then, we combine the Retinex algorithm for further contrast enhancement. To evaluate the effectiveness of the proposed method in qualitative and quantitative, it was compared with the adaptive histogram equalization method, the homomorphic filtering method and the SSR(Single-Scale-Retinex) method. Experimental results demonstrated that the presented algorithm can effectively enhance the contrast of CW-THZ image and obtain better visual effect.

  3. [Application of kalman filtering based on wavelet transform in ICP-AES].

    PubMed

    Qin, Xia; Shen, Lan-sun

    2002-12-01

    Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis. PMID:12914186

  4. Combining interior and exterior characteristics for remote sensing image denoising

    NASA Astrophysics Data System (ADS)

    Peng, Ni; Sun, Shujin; Wang, Runsheng; Zhong, Ping

    2016-04-01

    Remote sensing image denoising faces many challenges since a remote sensing image usually covers a wide area and thus contains complex contents. Using the patch-based statistical characteristics is a flexible method to improve the denoising performance. There are usually two kinds of statistical characteristics available: interior and exterior characteristics. Different statistical characteristics have their own strengths to restore specific image contents. Combining different statistical characteristics to use their strengths together may have the potential to improve denoising results. This work proposes a method combining statistical characteristics to adaptively select statistical characteristics for different image contents. The proposed approach is implemented through a new characteristics selection criterion learned over training data. Moreover, with the proposed combination method, this work develops a denoising algorithm for remote sensing images. Experimental results show that our method can make full use of the advantages of interior and exterior characteristics for different image contents and thus improve the denoising performance.

  5. Bleb Nucleation through Membrane Peeling

    NASA Astrophysics Data System (ADS)

    Alert, Ricard; Casademunt, Jaume

    2016-02-01

    We study the nucleation of blebs, i.e., protrusions arising from a local detachment of the membrane from the cortex of a cell. Based on a simple model of elastic linkers with force-dependent kinetics, we show that bleb nucleation is governed by membrane peeling. By this mechanism, the growth or shrinkage of a detached membrane patch is completely determined by the linker kinetics, regardless of the energetic cost of the detachment. We predict the critical nucleation radius for membrane peeling and the corresponding effective energy barrier. These may be typically smaller than those predicted by classical nucleation theory, implying a much faster nucleation. We also perform simulations of a continuum stochastic model of membrane-cortex adhesion to obtain the statistics of bleb nucleation times as a function of the stress on the membrane. The determinant role of membrane peeling changes our understanding of bleb nucleation and opens new directions in the study of blebs.

  6. Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary wavelet transform.

    PubMed

    Schlenke, Jan; Hildebrand, Lars; Moros, Javier; Laserna, J Javier

    2012-11-19

    Spectral signals are often corrupted by noise during their acquisition and transmission. Signal processing refers to a variety of operations that can be carried out on measurements in order to enhance the quality of information. In this sense, signal denoising is used to reduce noise distortions while keeping alterations of the important signal features to a minimum. The minimization of noise is a highly critical task since, in many cases, there is no prior knowledge of the signal or of the noise. In the context of denoising, wavelet transformation has become a valuable tool. The present paper proposes a noise reduction technique for suppressing noise in laser-induced breakdown spectroscopy (LIBS) signals using wavelet transform. An extension of the Donoho's scheme, which uses a redundant form of wavelet transformation and an adaptive threshold estimation method, is suggested. Capabilities and results achieved on denoising processes of artificial signals and actual spectroscopic data, both corrupted by noise with changing intensities, are presented. In order to better consolidate the gains so far achieved by the proposed strategy, a comparison with alternative approaches, as well as with traditional techniques, is also made.

  7. Noise Smoothing for Structural Vibration Test Signals Using an Improved Wavelet Thresholding Technique

    PubMed Central

    Yi, Ting-Hua; Li, Hong-Nan; Zhao, Xiao-Yan

    2012-01-01

    In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed by several processing parameters, including the type of wavelet, decomposition level, thresholding method, and threshold selection rules. To overcome the disadvantages of the traditional hard- and soft-thresholding methods, an improved thresholding technique called the sigmoid function-based thresholding scheme is presented. The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment. The performance of the proposed method is evaluated by computing the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) after denoising. Results reveal that the proposed method offers superior performance than the traditional methods no matter whether the signals have heavy or light noises embedded. PMID:23112652

  8. Wavelet Analyses and Applications

    ERIC Educational Resources Information Center

    Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.

    2009-01-01

    It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…

  9. Peeling Back the Layers

    NASA Technical Reports Server (NTRS)

    2004-01-01

    NASA's Mars Exploration Rover Spirit took this panoramic camera image of the rock target named 'Mazatzal' on sol 77 (March 22, 2004). It is a close-up look at the rock face and the targets that will be brushed and ground by the rock abrasion tool in upcoming sols.

    Mazatzal, like most rocks on Earth and Mars, has layers of material near its surface that provide clues about the history of the rock. Scientists believe that the top layer of Mazatzal is actually a coating of dust and possibly even salts. Under this light coating may be a more solid portion of the rock that has been chemically altered by weathering. Past this layer is the unaltered rock, which may give scientists the best information about how Mazatzal was formed.

    Because each layer reveals information about the formation and subsequent history of Mazatzal, it is important that scientists get a look at each of them. For this reason, they have developed a multi-part strategy to use the rock abrasion tool to systematically peel back Mazatzal's layers and analyze what's underneath with the rover's microscopic imager, and its Moessbauer and alpha particle X-ray spectrometers.

    The strategy began on sol 77 when scientists used the microscopic imager to get a closer look at targets on Mazatzal named 'New York,' 'Illinois' and 'Arizona.' These rock areas were targeted because they posed the best opportunity for successfully using the rock abrasion tool; Arizona also allowed for a close-up look at a range of tones. On sol 78, Spirit's rock abrasion tool will do a light brushing on the Illinois target to preserve some of the surface layers. Then, a brushing of the New York target should remove the top coating of any dust and salts and perhaps reveal the chemically altered rock underneath. Finally, on sol 79, the rock abrasion tool will be commanded to grind into the New York target, which will give scientists the best chance of observing Mazatzal's interior.

    The Mazatzal targets were named

  10. Portal imaging: Performance improvement in noise reduction by means of wavelet processing.

    PubMed

    González-López, Antonio; Morales-Sánchez, Juan; Larrey-Ruiz, Jorge; Bastida-Jumilla, María-Consuelo; Verdú-Monedero, Rafael

    2016-01-01

    This paper discusses the suitability, in terms of noise reduction, of various methods which can be applied to an image type often used in radiation therapy: the portal image. Among these methods, the analysis focuses on those operating in the wavelet domain. Wavelet-based methods tested on natural images--such as the thresholding of the wavelet coefficients, the minimization of the Stein unbiased risk estimator on a linear expansion of thresholds (SURE-LET), and the Bayes least-squares method using as a prior a Gaussian scale mixture (BLS-GSM method)--are compared with other methods that operate on the image domain--an adaptive Wiener filter and a nonlocal mean filter (NLM). For the assessment of the performance, the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), the Pearson correlation coefficient, and the Spearman rank correlation (ρ) coefficient are used. The performance of the wavelet filters and the NLM method are similar, but wavelet filters outperform the Wiener filter in terms of portal image denoising. It is shown how BLS-GSM and NLM filters produce the smoothest image, while keeping soft-tissue and bone contrast. As for the computational cost, filters using a decimated wavelet transform (decimated thresholding and SURE-LET) turn out to be the most efficient, with calculation times around 1 s. PMID:26602966

  11. Microbial degradation and utilization of cassava peel.

    PubMed

    Ofuya, C O; Nwajiuba, C J

    1990-06-01

    Cassava peel was readily degraded and utilized by a strain ofRhizopus growing in a solid-state fermentation. Growth was maximal at 45°C and was proportional to the degree of hydrolysis of the peel. The yield of biomass, as weight of dry mycellum from the reducing sugars of the peel, was 51%. After 72 h fermentation, the peel contained 76% moisture, 6% cellulose, 7% hemicellulose and 0.4% ash and the protein content had increased from 5.6% to 16%. These results suggest a possible economic value of cassava peel in the production of fungal biomass and feedstock.

  12. Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering.

    PubMed

    Singh, Omkar; Sunkaria, Ramesh Kumar

    2015-01-01

    Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942

  13. Application of wavelet packet transform to compressing Raman spectra data

    NASA Astrophysics Data System (ADS)

    Chen, Chen; Peng, Fei; Cheng, Qinghua; Xu, Dahai

    2008-12-01

    Abstract The Wavelet transform has been established with the Fourier transform as a data-processing method in analytical fields. The main fields of application are related to de-noising, compression, variable reduction, and signal suppression. Raman spectroscopy (RS) is characterized by the frequency excursion that can show the information of molecule. Every substance has its own feature Raman spectroscopy, which can analyze the structure, components, concentrations and some other properties of samples easily. RS is a powerful analytical tool for detection and identification. There are many databases of RS. But the data of Raman spectrum needs large space to storing and long time to searching. In this paper, Wavelet packet is chosen to compress Raman spectra data of some benzene series. The obtained results show that the energy retained is as high as 99.9% after compression, while the percentage for number of zeros is 87.50%. It was concluded that the Wavelet packet has significance in compressing the RS data.

  14. Wavelet-based pavement image compression and noise reduction

    NASA Astrophysics Data System (ADS)

    Zhou, Jian; Huang, Peisen S.; Chiang, Fu-Pen

    2005-08-01

    For any automated distress inspection system, typically a huge number of pavement images are collected. Use of an appropriate image compression algorithm can save disk space, reduce the saving time, increase the inspection distance, and increase the processing speed. In this research, a modified EZW (Embedded Zero-tree Wavelet) coding method, which is an improved version of the widely used EZW coding method, is proposed. This method, unlike the two-pass approach used in the original EZW method, uses only one pass to encode both the coordinates and magnitudes of wavelet coefficients. An adaptive arithmetic encoding method is also implemented to encode four symbols assigned by the modified EZW into binary bits. By applying a thresholding technique to terminate the coding process, the modified EZW coding method can compress the image and reduce noise simultaneously. The new method is much simpler and faster. Experimental results also show that the compression ratio was increased one and one-half times compared to the EZW coding method. The compressed and de-noised data can be used to reconstruct wavelet coefficients for off-line pavement image processing such as distress classification and quantification.

  15. Periodized Daubechies wavelets

    SciTech Connect

    Restrepo, J.M.; Leaf, G.K.; Schlossnagle, G.

    1996-03-01

    The properties of periodized Daubechies wavelets on [0,1] are detailed and counterparts which form a basis for L{sup 2}(R). Numerical examples illustrate the analytical estimates for convergence and demonstrated by comparison with Fourier spectral methods the superiority of wavelet projection methods for approximations. The analytical solution to inner products of periodized wavelets and their derivatives, which are known as connection coefficients, is presented, and their use ius illustrated in the approximation of two commonly used differential operators. The periodization of the connection coefficients in Galerkin schemes is presented in detail.

  16. Spike detection in human muscle sympathetic nerve activity using the kurtosis of stationary wavelet transform coefficients

    PubMed Central

    Brychta, Robert J.; Shiavi, Richard; Robertson, David; Diedrich, André

    2007-01-01

    The accurate assessment of autonomic sympathetic function is important in the diagnosis and study of various autonomic and cardiovascular disorders. Sympathetic function in humans can be assessed by recording the muscle sympathetic nerve activity, which is characterized by synchronous neuronal discharges separated by periods of neural silence dominated by colored Gaussian noise. The raw nerve activity is generally rectified, integrated, and quantified using the integrated burst rate or area. We propose an alternative quantification involving spike detection using a two-stage stationary wavelet transform (SWT) de-noising method. The SWT coefficients are first separated into noise-related and burst-related coefficients on the basis of their local kurtosis. The noise-related coefficients are then used to establish a threshold to identify spikes within the bursts. This method demonstrated better detection performance than an unsupervised amplitude discriminator and similar wavelet-based methods when confronted with simulated data of varying burst rate and signal to noise ratio. Additional validation on data acquired during a graded head-up tilt protocol revealed a strong correlation between the mean spike rate and the mean integrate burst rate (r = 0.85) and burst area rate (r = 0.91). In conclusion, the kurtosis-based wavelet de-noising technique is a potentially useful method of studying sympathetic nerve activity in humans. PMID:17083982

  17. Spike detection in human muscle sympathetic nerve activity using the kurtosis of stationary wavelet transform coefficients.

    PubMed

    Brychta, Robert J; Shiavi, Richard; Robertson, David; Diedrich, André

    2007-03-15

    The accurate assessment of autonomic sympathetic function is important in the diagnosis and study of various autonomic and cardiovascular disorders. Sympathetic function in humans can be assessed by recording the muscle sympathetic nerve activity, which is characterized by synchronous neuronal discharges separated by periods of neural silence dominated by colored Gaussian noise. The raw nerve activity is generally rectified, integrated, and quantified using the integrated burst rate or area. We propose an alternative quantification involving spike detection using a two-stage stationary wavelet transform (SWT) de-noising method. The SWT coefficients are first separated into noise-related and burst-related coefficients on the basis of their local kurtosis. The noise-related coefficients are then used to establish a threshold to identify spikes within the bursts. This method demonstrated better detection performance than an unsupervised amplitude discriminator and similar wavelet-based methods when confronted with simulated data of varying burst rate and signal to noise ratio. Additional validation on data acquired during a graded head-up tilt protocol revealed a strong correlation between the mean spike rate and the mean integrate burst rate (r=0.85) and burst area rate (r=0.91). In conclusion, the kurtosis-based wavelet de-noising technique is a potentially useful method of studying sympathetic nerve activity in humans.

  18. Wavelets and electromagnetics

    NASA Technical Reports Server (NTRS)

    Kempel, Leo C.

    1992-01-01

    Wavelets are an exciting new topic in applied mathematics and signal processing. This paper will provide a brief review of wavelets which are also known as families of functions with an emphasis on interpretation rather than rigor. We will derive an indirect use of wavelets for the solution of integral equations based techniques adapted from image processing. Examples for resistive strips will be given illustrating the effect of these techniques as well as their promise in reducing dramatically the requirement in order to solve an integral equation for large bodies. We also will present a direct implementation of wavelets to solve an integral equation. Both methods suggest future research topics and may hold promise for a variety of uses in computational electromagnetics.

  19. Adapted waveform analysis, wavelet packets, and local cosine libraries as a tool for image processing

    NASA Astrophysics Data System (ADS)

    Coifman, Ronald R.; Woog, Lionel J.

    1995-09-01

    Adapted wave form analysis, refers to a collection of FFT like adapted transform algorithms. Given an image these methods provide special matched collections of templates (orthonormal bases) enabling an efficient coding of the image. Perhaps the closest well known example of such coding method is provided by musical notation, where each segment of music is represented by a musical score made up of notes (templates) characterised by their duration, pitch, location and amplitude, our method corresponds to transcribing the music in as few notes as possible. The extension to images and video is straightforward we describe the image by collections of oscillatory patterns (paint brush strokes)of various sizes locations and amplitudes using a variety of orthogonal bases. These selected basis functions are chosen inside predefined libraries of oscillatory localized functions (trigonometric and wavelet-packets waveforms) so as to optimize the number of parameters needed to describe our object. These algorithms are of complexity N log N opening the door for a large range of applications in signal and image processing, such as compression, feature extraction denoising and enhancement. In particular we describe a class of special purpose compressions for fingerprint irnages, as well as denoising tools for texture and noise extraction. We start by relating traditional Fourier methods to wavelet, wavelet-packet based algorithms using a recent refinement of the windowed sine and cosine transforms. We will then derive an adapted local sine transform show it's relation to wavelet and wavelet-packet analysis and describe an analysis toolkit illustrating the merits of different adaptive and nonadaptive schemes.

  20. A new method for mobile phone image denoising

    NASA Astrophysics Data System (ADS)

    Jin, Lianghai; Jin, Min; Li, Xiang; Xu, Xiangyang

    2015-12-01

    Images captured by mobile phone cameras via pipeline processing usually contain various kinds of noises, especially granular noise with different shapes and sizes in both luminance and chrominance channels. In chrominance channels, noise is closely related to image brightness. To improve image quality, this paper presents a new method to denoise such mobile phone images. The proposed scheme converts the noisy RGB image to luminance and chrominance images, which are then denoised by a common filtering framework. The common filtering framework processes a noisy pixel by first excluding the neighborhood pixels that significantly deviate from the (vector) median and then utilizing the other neighborhood pixels to restore the current pixel. In the framework, the strength of chrominance image denoising is controlled by image brightness. The experimental results show that the proposed method obviously outperforms some other representative denoising methods in terms of both objective measure and visual evaluation.

  1. Edge-preserving image denoising via optimal color space projection.

    PubMed

    Lian, Nai-Xiang; Zagorodnov, Vitali; Tan, Yap-Peng

    2006-09-01

    Denoising of color images can be done on each color component independently. Recent work has shown that exploiting strong correlation between high-frequency content of different color components can improve the denoising performance. We show that for typical color images high correlation also means similarity, and propose to exploit this strong intercolor dependency using an optimal luminance/color-difference space projection. Experimental results confirm that performing denoising on the projected color components yields superior denoising performance, both in peak signal-to-noise ratio and visual quality sense, compared to that of existing solutions. We also develop a novel approach to estimate directly from the noisy image data the image and noise statistics, which are required to determine the optimal projection.

  2. Image denoising filter based on patch-based difference refinement

    NASA Astrophysics Data System (ADS)

    Park, Sang Wook; Kang, Moon Gi

    2012-06-01

    In the denoising literature, research based on the nonlocal means (NLM) filter has been done and there have been many variations and improvements regarding weight function and parameter optimization. Here, a NLM filter with patch-based difference (PBD) refinement is presented. PBD refinement, which is the weighted average of the PBD values, is performed with respect to the difference images of all the locations in a refinement kernel. With refined and denoised PBD values, pattern adaptive smoothing threshold and noise suppressed NLM filter weights are calculated. Owing to the refinement of the PBD values, the patterns are divided into flat regions and texture regions by comparing the sorted values in the PBD domain to the threshold value including the noise standard deviation. Then, two different smoothing thresholds are utilized for each region denoising, respectively, and the NLM filter is applied finally. Experimental results of the proposed scheme are shown in comparison with several state-of-the-arts NLM based denoising methods.

  3. Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

    NASA Astrophysics Data System (ADS)

    Khalil, Bahaa; Broda, Stefan; Adamowski, Jan; Ozga-Zielinski, Bogdan; Donohoe, Amanda

    2015-02-01

    Several groundwater-level forecasting studies have shown that data-driven models are simpler, faster to develop, and provide more accurate and precise results than physical or numerical-based models. Five data-driven models were examined for the forecasting of groundwater levels as a result of recharge via tailings from an abandoned mine in Quebec, Canada, for lead times of 1 day, 1 week and 1 month. The five models are: a multiple linear regression (MLR); an artificial neural network (ANN); two models that are based on de-noising the model predictors using the wavelet-transform (W-MLR, W-ANN); and a W-ensemble ANN (W-ENN) model. The tailing recharge, total precipitation, and mean air temperature were used as predictors. The ANN models performed better than the MLR models, and both MLR and ANN models performed significantly better after de-noising the predictors using wavelet-transforms. Overall, the W-ENN model performed best for each of the three lead times. These results highlight the ability of wavelet-transforms to decompose non-stationary data into discrete wavelet-components, highlighting cyclic patterns and trends in the time-series at varying temporal scales, rendering the data readily usable in forecasting. The good performance of the W-ENN model highlights the usefulness of ensemble modeling, which ensures model robustness along with improved reliability by reducing variance.

  4. Wavelets meet genetic imaging

    NASA Astrophysics Data System (ADS)

    Wang, Yu-Ping

    2005-08-01

    Genetic image analysis is an interdisciplinary area, which combines microscope image processing techniques with the use of biochemical probes for the detection of genetic aberrations responsible for cancers and genetic diseases. Recent years have witnessed parallel and significant progress in both image processing and genetics. On one hand, revolutionary multiscale wavelet techniques have been developed in signal processing and applied mathematics in the last decade, providing sophisticated tools for genetic image analysis. On the other hand, reaping the fruit of genome sequencing, high resolution genetic probes have been developed to facilitate accurate detection of subtle and cryptic genetic aberrations. In the meantime, however, they bring about computational challenges for image analysis. In this paper, we review the fruitful interaction between wavelets and genetic imaging. We show how wavelets offer a perfect tool to address a variety of chromosome image analysis problems. In fact, the same word "subband" has been used in the nomenclature of cytogenetics to describe the multiresolution banding structure of the chromosome, even before its appearance in the wavelet literature. The application of wavelets to chromosome analysis holds great promise in addressing several computational challenges in genetics. A variety of real world examples such as the chromosome image enhancement, compression, registration and classification will be demonstrated. These examples are drawn from fluorescence in situ hybridization (FISH) and microarray (gene chip) imaging experiments, which indicate the impact of wavelets on the diagnosis, treatments and prognosis of cancers and genetic diseases.

  5. Principal component analysis in the wavelet domain: new features for underwater object recognition

    NASA Astrophysics Data System (ADS)

    Okimoto, Gordon S.; Lemonds, David W.

    1999-08-01

    Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.

  6. A novel 3D wavelet based filter for visualizing features in noisy biological data

    SciTech Connect

    Moss, W C; Haase, S; Lyle, J M; Agard, D A; Sedat, J W

    2005-01-05

    We have developed a 3D wavelet-based filter for visualizing structural features in volumetric data. The only variable parameter is a characteristic linear size of the feature of interest. The filtered output contains only those regions that are correlated with the characteristic size, thus denoising the image. We demonstrate the use of the filter by applying it to 3D data from a variety of electron microscopy samples including low contrast vitreous ice cryogenic preparations, as well as 3D optical microscopy specimens.

  7. A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry

    NASA Astrophysics Data System (ADS)

    Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

    2014-03-01

    This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

  8. Effect of denoising on supervised lung parenchymal clusters

    NASA Astrophysics Data System (ADS)

    Jayamani, Padmapriya; Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

    2012-03-01

    Denoising is a critical preconditioning step for quantitative analysis of medical images. Despite promises for more consistent diagnosis, denoising techniques are seldom explored in clinical settings. While this may be attributed to the esoteric nature of the parameter sensitve algorithms, lack of quantitative measures on their ecacy to enhance the clinical decision making is a primary cause of physician apathy. This paper addresses this issue by exploring the eect of denoising on the integrity of supervised lung parenchymal clusters. Multiple Volumes of Interests (VOIs) were selected across multiple high resolution CT scans to represent samples of dierent patterns (normal, emphysema, ground glass, honey combing and reticular). The VOIs were labeled through consensus of four radiologists. The original datasets were ltered by multiple denoising techniques (median ltering, anisotropic diusion, bilateral ltering and non-local means) and the corresponding ltered VOIs were extracted. Plurality of cluster indices based on multiple histogram-based pair-wise similarity measures were used to assess the quality of supervised clusters in the original and ltered space. The resultant rank orders were analyzed using the Borda criteria to nd the denoising-similarity measure combination that has the best cluster quality. Our exhaustive analyis reveals (a) for a number of similarity measures, the cluster quality is inferior in the ltered space; and (b) for measures that benet from denoising, a simple median ltering outperforms non-local means and bilateral ltering. Our study suggests the need to judiciously choose, if required, a denoising technique that does not deteriorate the integrity of supervised clusters.

  9. Comparative study of adsorption of Pb(II) on native garlic peel and mercerized garlic peel.

    PubMed

    Liu, Wei; Liu, Yifeng; Tao, Yaqi; Yu, Youjie; Jiang, Hongmei; Lian, Hongzhen

    2014-02-01

    A comparative study using native garlic peel and mercerized garlic peel as adsorbents for the removal of Pb(2+) has been proposed. Under the optimized pH, contact time, and adsorbent dosage, the adsorption capacity of garlic peel after mercerization was increased 2.1 times and up to 109.05 mg g(-1). The equilibrium sorption data for both garlic peels fitted well with Langmuir adsorption isotherm, and the adsorbent-adsorbate kinetics followed pseudo-second-order model. These both garlic peels were characterized by elemental analysis, Fourier transform infrared spectrometry (FT-IR), and scanning electron microscopy, and the results indicated that mercerized garlic peel offers more little pores acted as adsorption sites than native garlic peel and has lower polymerization and crystalline and more accessible functional hydroxyl groups, which resulted in higher adsorption capacity than native garlic peel. The FT-IR and X-ray photoelectron spectroscopy analyses of both garlic peels before and after loaded with Pb(2+) further illustrated that lead was adsorbed on the through chelation between Pb(2+) and O atom existed on the surface of garlic peels. These results described above showed that garlic peel after mercerization can be a more attractive adsorbent due to its faster sorption uptake and higher capacity.

  10. Prediction of processing tomato peeling outcomes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...

  11. Food peeling: conventional and new approaches

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Peeling is an important unit operation in food processing that prepares fruits and vegetables for subsequent processes through removal of inedible or undesirable rind or skin. This chapter covers an exhaustive discussion on advancement in peeling technologies of fruits and vegetables from different ...

  12. Pomegranate peel and peel extracts: chemistry and food features.

    PubMed

    Akhtar, Saeed; Ismail, Tariq; Fraternale, Daniele; Sestili, Piero

    2015-05-01

    The present review focuses on the nutritional, functional and anti-infective properties of pomegranate (Punica granatum L.) peel (PoP) and peel extract (PoPx) and on their applications as food additives, functional food ingredients or biologically active components in nutraceutical preparations. Due to their well-known ethnomedical relevance and chemical features, the biomolecules available in PoP and PoPx have been proposed, for instance, as substitutes of synthetic food additives, as nutraceuticals and chemopreventive agents. However, because of their astringency and anti-nutritional properties, PoP and PoPx are not yet considered as ingredients of choice in food systems. Indeed, considering the prospects related to both their health promoting activity and chemical features, the nutritional and nutraceutical potential of PoP and PoPx seems to be still underestimated. The present review meticulously covers the wide range of actual and possible applications (food preservatives, stabilizers, supplements, prebiotics and quality enhancers) of PoP and PoPx components in various food products. Given the overall properties of PoP and PoPx, further investigations in toxicological and sensory aspects of PoP and PoPx should be encouraged to fully exploit the health promoting and technical/economic potential of these waste materials as food supplements. PMID:25529700

  13. Pomegranate peel and peel extracts: chemistry and food features.

    PubMed

    Akhtar, Saeed; Ismail, Tariq; Fraternale, Daniele; Sestili, Piero

    2015-05-01

    The present review focuses on the nutritional, functional and anti-infective properties of pomegranate (Punica granatum L.) peel (PoP) and peel extract (PoPx) and on their applications as food additives, functional food ingredients or biologically active components in nutraceutical preparations. Due to their well-known ethnomedical relevance and chemical features, the biomolecules available in PoP and PoPx have been proposed, for instance, as substitutes of synthetic food additives, as nutraceuticals and chemopreventive agents. However, because of their astringency and anti-nutritional properties, PoP and PoPx are not yet considered as ingredients of choice in food systems. Indeed, considering the prospects related to both their health promoting activity and chemical features, the nutritional and nutraceutical potential of PoP and PoPx seems to be still underestimated. The present review meticulously covers the wide range of actual and possible applications (food preservatives, stabilizers, supplements, prebiotics and quality enhancers) of PoP and PoPx components in various food products. Given the overall properties of PoP and PoPx, further investigations in toxicological and sensory aspects of PoP and PoPx should be encouraged to fully exploit the health promoting and technical/economic potential of these waste materials as food supplements.

  14. A fast optimization transfer algorithm for image inpainting in wavelet domains.

    PubMed

    Chan, Raymond H; Wen, You-Wei; Yip, Andy M

    2009-07-01

    A wavelet inpainting problem refers to the problem of filling in missing wavelet coefficients in an image. A variational approach was used by Chan et al. The resulting functional was minimized by the gradient descent method. In this paper, we use an optimization transfer technique which involves replacing their univariate functional by a bivariate functional by adding an auxiliary variable. Our bivariate functional can be minimized easily by alternating minimization: for the auxiliary variable, the minimum has a closed form solution, and for the original variable, the minimization problem can be formulated as a classical total variation (TV) denoising problem and, hence, can be solved efficiently using a dual formulation. We show that our bivariate functional is equivalent to the original univariate functional. We also show that our alternating minimization is convergent. Numerical results show that the proposed algorithm is very efficient and outperforms that of Chan et al.

  15. Remote sensing image denoising application by generalized morphological component analysis

    NASA Astrophysics Data System (ADS)

    Yu, Chong; Chen, Xiong

    2014-12-01

    In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.

  16. GPU-accelerated denoising of 3D magnetic resonance images

    SciTech Connect

    Howison, Mark; Wes Bethel, E.

    2014-05-29

    The raw computational power of GPU accelerators enables fast denoising of 3D MR images using bilateral filtering, anisotropic diffusion, and non-local means. In practice, applying these filtering operations requires setting multiple parameters. This study was designed to provide better guidance to practitioners for choosing the most appropriate parameters by answering two questions: what parameters yield the best denoising results in practice? And what tuning is necessary to achieve optimal performance on a modern GPU? To answer the first question, we use two different metrics, mean squared error (MSE) and mean structural similarity (MSSIM), to compare denoising quality against a reference image. Surprisingly, the best improvement in structural similarity with the bilateral filter is achieved with a small stencil size that lies within the range of real-time execution on an NVIDIA Tesla M2050 GPU. Moreover, inappropriate choices for parameters, especially scaling parameters, can yield very poor denoising performance. To answer the second question, we perform an autotuning study to empirically determine optimal memory tiling on the GPU. The variation in these results suggests that such tuning is an essential step in achieving real-time performance. These results have important implications for the real-time application of denoising to MR images in clinical settings that require fast turn-around times.

  17. Evaluation of denoising algorithms for biological electron tomography.

    PubMed

    Narasimha, Rajesh; Aganj, Iman; Bennett, Adam E; Borgnia, Mario J; Zabransky, Daniel; Sapiro, Guillermo; McLaughlin, Steven W; Milne, Jacqueline L S; Subramaniam, Sriram

    2008-10-01

    Tomograms of biological specimens derived using transmission electron microscopy can be intrinsically noisy due to the use of low electron doses, the presence of a "missing wedge" in most data collection schemes, and inaccuracies arising during 3D volume reconstruction. Before tomograms can be interpreted reliably, for example, by 3D segmentation, it is essential that the data be suitably denoised using procedures that can be individually optimized for specific data sets. Here, we implement a systematic procedure to compare various nonlinear denoising techniques on tomograms recorded at room temperature and at cryogenic temperatures, and establish quantitative criteria to select a denoising approach that is most relevant for a given tomogram. We demonstrate that using an appropriate denoising algorithm facilitates robust segmentation of tomograms of HIV-infected macrophages and Bdellovibrio bacteria obtained from specimens at room and cryogenic temperatures, respectively. We validate this strategy of automated segmentation of optimally denoised tomograms by comparing its performance with manual extraction of key features from the same tomograms.

  18. Adaptively Tuned Iterative Low Dose CT Image Denoising.

    PubMed

    Hashemi, SayedMasoud; Paul, Narinder S; Beheshti, Soosan; Cobbold, Richard S C

    2015-01-01

    Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction. PMID:26089972

  19. Adaptively Tuned Iterative Low Dose CT Image Denoising

    PubMed Central

    Hashemi, SayedMasoud; Paul, Narinder S.; Beheshti, Soosan; Cobbold, Richard S. C.

    2015-01-01

    Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction. PMID:26089972

  20. Signal quality enhancement using higher order wavelets for ultrasonic TOFD signals from austenitic stainless steel welds.

    PubMed

    Praveen, Angam; Vijayarekha, K; Abraham, Saju T; Venkatraman, B

    2013-09-01

    Time of flight diffraction (TOFD) technique is a well-developed ultrasonic non-destructive testing (NDT) method and has been applied successfully for accurate sizing of defects in metallic materials. This technique was developed in early 1970s as a means for accurate sizing and positioning of cracks in nuclear components became very popular in the late 1990s and is today being widely used in various industries for weld inspection. One of the main advantages of TOFD is that, apart from fast technique, it provides higher probability of detection for linear defects. Since TOFD is based on diffraction of sound waves from the extremities of the defect compared to reflection from planar faces as in pulse echo and phased array, the resultant signal would be quite weak and signal to noise ratio (SNR) low. In many cases the defect signal is submerged in this noise making it difficult for detection, positioning and sizing. Several signal processing methods such as digital filtering, Split Spectrum Processing (SSP), Hilbert Transform and Correlation techniques have been developed in order to suppress unwanted noise and enhance the quality of the defect signal which can thus be used for characterization of defects and the material. Wavelet Transform based thresholding techniques have been applied largely for de-noising of ultrasonic signals. However in this paper, higher order wavelets are used for analyzing the de-noising performance for TOFD signals obtained from Austenitic Stainless Steel welds. It is observed that higher order wavelets give greater SNR improvement compared to the lower order wavelets.

  1. Biomethanization of orange peel waste.

    PubMed

    Martín, M A; Siles, J A; Chica, A F; Martín, A

    2010-12-01

    Recent research has demonstrated that orange peel waste is a potentially valuable resource that can be developed into high value products such as methane. Following a pre-treatment to extract D-limonene, the anaerobic digestion of orange peel waste was evaluated at laboratory and pilot scale under mesophilic and thermophilic conditions. D-limonene removals of 70% were reached with pre-treatment. The results showed the convenience of thermophilic conditions for treating this waste as the methane production rate and biodegradability were higher than at mesophilic temperature. At pilot scale, a thermophilic continuously stirred-tank reactor working in semi-continuous mode was employed. The OLR was found to be in the range of 1.20-3.67 kg COD/m(3) d; the most appropriate range for working under stable conditions at SRT of 25 d. The methane yield coefficient was found to be 0.27-0.29 L(STP)CH(4)/g added COD and the biodegradability 84-90% under these conditions. However, acidification occurred at the highest OLR.

  2. Non-local MRI denoising using random sampling.

    PubMed

    Hu, Jinrong; Zhou, Jiliu; Wu, Xi

    2016-09-01

    In this paper, we propose a random sampling non-local mean (SNLM) algorithm to eliminate noise in 3D MRI datasets. Non-local means (NLM) algorithms have been implemented efficiently for MRI denoising, but are always limited by high computational complexity. Compared to conventional methods, which raster through the entire search window when computing similarity weights, the proposed SNLM algorithm randomly selects a small subset of voxels which dramatically decreases the computational burden, together with competitive denoising result. Moreover, structure tensor which encapsulates high-order information was introduced as an optimal sampling pattern for further improvement. Numerical experiments demonstrated that the proposed SNLM method can get a good balance between denoising quality and computation efficiency. At a relative sampling ratio (i.e. ξ=0.05), SNLM can remove noise as effectively as full NLM, meanwhile the running time can be reduced to 1/20 of NLM's. PMID:27114338

  3. Total Variation Denoising and Support Localization of the Gradient

    NASA Astrophysics Data System (ADS)

    Chambolle, A.; Duval, V.; Peyré, G.; Poon, C.

    2016-10-01

    This paper describes the geometrical properties of the solutions to the total variation denoising method. A folklore statement is that this method is able to restore sharp edges, but at the same time, might introduce some staircasing (i.e. “fake” edges) in flat areas. Quite surprisingly, put aside numerical evidences, almost no theoretical result are available to backup these claims. The first contribution of this paper is a precise mathematical definition of the “extended support” (associated to the noise-free image) of TV denoising. This is intuitively the region which is unstable and will suffer from the staircasing effect. Our main result shows that the TV denoising method indeed restores a piece-wise constant image outside a small tube surrounding the extended support. Furthermore, the radius of this tube shrinks toward zero as the noise level vanishes and in some cases, an upper bound on the convergence rate is given.

  4. Wavelet evolutionary network for complex-constrained portfolio rebalancing

    NASA Astrophysics Data System (ADS)

    Suganya, N. C.; Vijayalakshmi Pai, G. A.

    2012-07-01

    Portfolio rebalancing problem deals with resetting the proportion of different assets in a portfolio with respect to changing market conditions. The constraints included in the portfolio rebalancing problem are basic, cardinality, bounding, class and proportional transaction cost. In this study, a new heuristic algorithm named wavelet evolutionary network (WEN) is proposed for the solution of complex-constrained portfolio rebalancing problem. Initially, the empirical covariance matrix, one of the key inputs to the problem, is estimated using the wavelet shrinkage denoising technique to obtain better optimal portfolios. Secondly, the complex cardinality constraint is eliminated using k-means cluster analysis. Finally, WEN strategy with logical procedures is employed to find the initial proportion of investment in portfolio of assets and also rebalance them after certain period. Experimental studies of WEN are undertaken on Bombay Stock Exchange, India (BSE200 index, period: July 2001-July 2006) and Tokyo Stock Exchange, Japan (Nikkei225 index, period: March 2002-March 2007) data sets. The result obtained using WEN is compared with the only existing counterpart named Hopfield evolutionary network (HEN) strategy and also verifies that WEN performs better than HEN. In addition, different performance metrics and data envelopment analysis are carried out to prove the robustness and efficiency of WEN over HEN strategy.

  5. Quantitative improvement in geology interpretation from remotely sensed data by denoising the haze

    NASA Astrophysics Data System (ADS)

    Yang, Hai-ping; Liu, Xiu-guo; Liu, Fu-jiang; Wu, Guo-ping; Shi, Jin-ping; Mei, Lin-lu

    2008-12-01

    The development of remote geological interpretation technology is booming during recent years. However, there is a significant obstacle for extracting geology information from remote sensing imagery--the presence of clouds and their shadows. Diverse techniques have been proposed including different algorithms such as filtering algorithm and multi-temporal cloud removing algorithm to solve the problem. This paper presents a modified solution to denoise the haze, based on ETM+ imagery. First of all, wavelet transform is applied to Band1, Band2 and Band3 imagery to determine the clear region and different levels of cloud regions. Then all pixels of the ETM+ imagery are classified to specific cover types after the cluster analysis of band4, Band5 and Band7. At last, the mean reflectance matching is performed in the first three bands separately according to different cover types in both clear region and cloud region. Above all, the method is implemented by IDL. The results show that this modified method not only can quantitatively determine the cloud area but also can remove cloud from imagery efficiently. Moreover, compared with the homomorphic filtering method, the experiment results of the proposed method is much more satisfying in Geology Interpretation.

  6. Sinogram denoising via simultaneous sparse representation in learned dictionaries

    NASA Astrophysics Data System (ADS)

    Karimi, Davood; Ward, Rabab K.

    2016-05-01

    Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster.

  7. Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

    PubMed

    Zeng, Xianhua; Bian, Wei; Liu, Wei; Shen, Jialie; Tao, Dacheng

    2015-11-01

    Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.

  8. Patterning, Prestress, and Peeling Dynamics of Myocytes

    PubMed Central

    Griffin, Maureen A.; Engler, Adam J.; Barber, Thomas A.; Healy, Kevin E.; Sweeney, H. Lee; Discher, Dennis E.

    2004-01-01

    As typical anchorage-dependent cells myocytes must balance contractility against adequate adhesion. Skeletal myotubes grown as isolated strips from myoblasts on micropatterned glass exhibited spontaneous peeling after one end of the myotube was mechanically detached. Such results indicate the development of a prestress in the cells. To assess this prestress and study the dynamic adhesion strength of single myocytes, the shear stress of fluid aspirated into a large-bore micropipette was then used to forcibly peel myotubes. The velocity at which cells peeled from the surface, Vpeel, was measured as a continuously increasing function of the imposed tension, Tpeel, which ranges from ∼0 to 50 nN/μm. For each cell, peeling proved highly heterogeneous, with Vpeel fluctuating between 0 μm/s (∼80% of time) and ∼10 μm/s. Parallel studies of smooth muscle cells expressing GFP-paxillin also exhibited a discontinuous peeling in which focal adhesions fractured above sites of strong attachment (when pressure peeled using a small-bore pipette). The peeling approaches described here lend insight into the contractile-adhesion balance and can be used to study the real-time dynamics of stressed adhesions through both physical detection and the use of GFP markers; the methods should prove useful in comparing normal versus dystrophic muscle cells. PMID:14747355

  9. A wavelet-based Markov random field segmentation model in segmenting microarray experiments.

    PubMed

    Athanasiadis, Emmanouil; Cavouras, Dionisis; Kostopoulos, Spyros; Glotsos, Dimitris; Kalatzis, Ioannis; Nikiforidis, George

    2011-12-01

    In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.

  10. [A novel method to determine the redshifts of active galaxies based on wavelet transform].

    PubMed

    Tu, Liang-Ping; Luo, A-Li; Jiang, Bin; Wei, Peng; Zhao, Yong-Heng; Liu, Rong

    2012-10-01

    Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.

  11. GPU-Accelerated Denoising in 3D (GD3D)

    2013-10-01

    The raw computational power GPU Accelerators enables fast denoising of 3D MR images using bilateral filtering, anisotropic diffusion, and non-local means. This software addresses two facets of this promising application: what tuning is necessary to achieve optimal performance on a modern GPU? And what parameters yield the best denoising results in practice? To answer the first question, the software performs an autotuning step to empirically determine optimal memory blocking on the GPU. To answer themore » second, it performs a sweep of algorithm parameters to determine the combination that best reduces the mean squared error relative to a noiseless reference image.« less

  12. Mouse EEG spike detection based on the adapted continuous wavelet transform

    NASA Astrophysics Data System (ADS)

    Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

    2016-04-01

    Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

  13. Basis Selection for Wavelet Regression

    NASA Technical Reports Server (NTRS)

    Wheeler, Kevin R.; Lau, Sonie (Technical Monitor)

    1998-01-01

    A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods.

  14. Interpreting honeycomb climbing-drum peel tests

    NASA Technical Reports Server (NTRS)

    Ferdie, R. D.

    1977-01-01

    Drum-peel tests are made more meaningful by use of approximations to derive analytical expressions relating failures due to bond flatwise tension, inplane tension, and shear, to adhesive weight and method of bond cure.

  15. The Peel Inlet-Harvey Estuary Study.

    ERIC Educational Resources Information Center

    Walker, Warren; Black, Ronald

    1979-01-01

    Describes how the department of physics of the Western Australian Institute of Technology (WAIT) has been involved in the Peel Inlet-Harvey Estuary study. An appendix which presents the departmental approach to curriculum matters is also included. (HM)

  16. Pixon Based Image Denoising Scheme by Preserving Exact Edge Locations

    NASA Astrophysics Data System (ADS)

    Srikrishna, Atluri; Reddy, B. Eswara; Pompapathi, Manasani

    2016-09-01

    Denoising of an image is an essential step in many image processing applications. In any image de-noising algorithm, it is a major concern to keep interesting structures of the image like abrupt changes in image intensity values (edges). In this paper an efficient algorithm for image de-noising is proposed that obtains integrated and consecutive original image from noisy image using diffusion equations in pixon domain. The process mainly consists of two steps. In first step, the pixons for noisy image are obtained by using K-means clustering process and next step includes applying diffusion equations on the pixonal model of the image to obtain new intensity values for the restored image. The process has been applied on a variety of standard images and the objective fidelity has been compared with existing algorithms. The experimental results show that the proposed algorithm has a better performance by preserving edge details compared in terms of Figure of Merit and improved Peak-to-Signal-Noise-Ratio value. The proposed method brings out a denoising technique which preserves edge details.

  17. Perceptually Lossless Wavelet Compression

    NASA Technical Reports Server (NTRS)

    Watson, Andrew B.; Yang, Gloria Y.; Solomon, Joshua A.; Villasenor, John

    1996-01-01

    The Discrete Wavelet Transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter, which we call DWT uniform quantization noise. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2(exp -1), where r is display visual resolution in pixels/degree, and L is the wavelet level. Amplitude thresholds increase rapidly with spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We propose a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a 'perceptually lossless' quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

  18. Denoising and dimensionality reduction of genomic data

    NASA Astrophysics Data System (ADS)

    Capobianco, Enrico

    2005-05-01

    Genomics represents a challenging research field for many quantitative scientists, and recently a vast variety of statistical techniques and machine learning algorithms have been proposed and inspired by cross-disciplinary work with computational and systems biologists. In genomic applications, the researcher deals with noisy and complex high-dimensional feature spaces; a wealth of genes whose expression levels are experimentally measured, can often be observed for just a few time points, thus limiting the available samples. This unbalanced combination suggests that it might be hard for standard statistical inference techniques to come up with good general solutions, likewise for machine learning algorithms to avoid heavy computational work. Thus, one naturally turns to two major aspects of the problem: sparsity and intrinsic dimensionality. These two aspects are studied in this paper, where for both denoising and dimensionality reduction, a very efficient technique, i.e., Independent Component Analysis, is used. The numerical results are very promising, and lead to a very good quality of gene feature selection, due to the signal separation power enabled by the decomposition technique. We investigate how the use of replicates can improve these results, and deal with noise through a stabilization strategy which combines the estimated components and extracts the most informative biological information from them. Exploiting the inherent level of sparsity is a key issue in genetic regulatory networks, where the connectivity matrix needs to account for the real links among genes and discard many redundancies. Most experimental evidence suggests that real gene-gene connections represent indeed a subset of what is usually mapped onto either a huge gene vector or a typically dense and highly structured network. Inferring gene network connectivity from the expression levels represents a challenging inverse problem that is at present stimulating key research in biomedical

  19. Dictionary-based image denoising for dual energy computed tomography

    NASA Astrophysics Data System (ADS)

    Mechlem, Korbinian; Allner, Sebastian; Mei, Kai; Pfeiffer, Franz; Noël, Peter B.

    2016-03-01

    Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.

  20. Denoising surface renewal flux density measurements

    NASA Astrophysics Data System (ADS)

    Shapland, T.; Paw U, K.; Snyder, R. L.; McElrone, A.; Calderon Orellana, A.; Williams, L.

    2012-12-01

    When combined with net radiation and ground heat flux density measurements, surface renewal sensible heat flux density measurements can be used to obtain latent heat flux density, and therefore evapotranspiration, via the energy balance residual. Surface renewal is based on analyzing the energy and mass budget of air parcels that interact with plant canopies. The air parcels are manifested as ramp-like shapes in turbulent scalar time series data, and the amplitude and period of the ramps are used to calculate the flux densities. The root mean square error between calibrated surface renewal and eddy covariance is generally twice the root mean square error between two eddy covariance systems. In this presentation, we evaluate the efficacy of various methods for reducing the random error in surface renewal sensible heat flux density measurements. These methods include signal de-spiking, conventional low-pass filtering, wavelet-based filtering, ramp signal to noise thresholds, ramp period scaling, novel rearrangements of the Van Atta procedure (Arch Mech 29:161-171, 1977) for resolving the ramp amplitude and ramp period, sensor replication, and optimization of sensor placement.

  1. Wavelet-Based Grid Generation

    NASA Technical Reports Server (NTRS)

    Jameson, Leland

    1996-01-01

    Wavelets can provide a basis set in which the basis functions are constructed by dilating and translating a fixed function known as the mother wavelet. The mother wavelet can be seen as a high pass filter in the frequency domain. The process of dilating and expanding this high-pass filter can be seen as altering the frequency range that is 'passed' or detected. The process of translation moves this high-pass filter throughout the domain, thereby providing a mechanism to detect the frequencies or scales of information at every location. This is exactly the type of information that is needed for effective grid generation. This paper provides motivation to use wavelets for grid generation in addition to providing the final product: source code for wavelet-based grid generation.

  2. A generalized wavelet extrema representation

    SciTech Connect

    Lu, Jian; Lades, M.

    1995-10-01

    The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.

  3. An Introduction to Wavelet Theory and Analysis

    SciTech Connect

    Miner, N.E.

    1998-10-01

    This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides tiormation about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, define common wavelet terminology and describe wavelet amdysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary.

  4. Wavelet application to the time series analysis of DORIS station coordinates

    NASA Astrophysics Data System (ADS)

    Bessissi, Zahia; Terbeche, Mekki; Ghezali, Boualem

    2009-06-01

    The topic developed in this article relates to the residual time series analysis of DORIS station coordinates using the wavelet transform. Several analysis techniques, already developed in other disciplines, were employed in the statistical study of the geodetic time series of stations. The wavelet transform allows one, on the one hand, to provide temporal and frequential parameter residual signals, and on the other hand, to determine and quantify systematic signals such as periodicity and tendency. Tendency is the change in short or long term signals; it is an average curve which represents the general pace of the signal evolution. On the other hand, periodicity is a process which is repeated, identical to itself, after a time interval called the period. In this context, the topic of this article consists, on the one hand, in determining the systematic signals by wavelet analysis of time series of DORIS station coordinates, and on the other hand, in applying the denoising signal to the wavelet packet, which makes it possible to obtain a well-filtered signal, smoother than the original signal. The DORIS data used in the treatment are a set of weekly residual time series from 1993 to 2004 from eight stations: DIOA, COLA, FAIB, KRAB, SAKA, SODB, THUB and SYPB. It is the ign03wd01 solution expressed in stcd format, which is derived by the IGN/JPL analysis center. Although these data are not very recent, the goal of this study is to detect the contribution of the wavelet analysis method on the DORIS data, compared to the other analysis methods already studied.

  5. Diffusion weighted image denoising using overcomplete local PCA.

    PubMed

    Manjón, José V; Coupé, Pierrick; Concha, Luis; Buades, Antonio; Collins, D Louis; Robles, Montserrat

    2013-01-01

    Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters. PMID:24019889

  6. Diffusion Weighted Image Denoising Using Overcomplete Local PCA

    PubMed Central

    Manjón, José V.; Coupé, Pierrick; Concha, Luis; Buades, Antonio; Collins, D. Louis; Robles, Montserrat

    2013-01-01

    Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters. PMID:24019889

  7. Point Set Denoising Using Bootstrap-Based Radial Basis Function.

    PubMed

    Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad

    2016-01-01

    This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.

  8. Point Set Denoising Using Bootstrap-Based Radial Basis Function

    PubMed Central

    Ramli, Ahmad; Abd. Majid, Ahmad

    2016-01-01

    This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. PMID:27315105

  9. Denoising Sparse Images from GRAPPA using the Nullspace Method (DESIGN)

    PubMed Central

    Weller, Daniel S.; Polimeni, Jonathan R.; Grady, Leo; Wald, Lawrence L.; Adalsteinsson, Elfar; Goyal, Vivek K

    2011-01-01

    To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with GRAPPA alone, the Denoising of Sparse Images from GRAPPA using the Nullspace method (DESIGN) is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DESIGN are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), PSNR, and noise amplification (g-factors) as measured using the pseudo-multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g-factors suggest that DESIGN mitigates noise amplification better than both GRAPPA and L1 SPIR-iT (the latter limited here by uniform undersampling). PMID:22213069

  10. Streak image denoising and segmentation using adaptive Gaussian guided filter.

    PubMed

    Jiang, Zhuocheng; Guo, Baoping

    2014-09-10

    In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.

  11. Streak image denoising and segmentation using adaptive Gaussian guided filter.

    PubMed

    Jiang, Zhuocheng; Guo, Baoping

    2014-09-10

    In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance. PMID:25321679

  12. Improvement of wavelet threshold filtered back-projection image reconstruction algorithm

    NASA Astrophysics Data System (ADS)

    Ren, Zhong; Liu, Guodong; Huang, Zhen

    2014-11-01

    Image reconstruction technique has been applied into many fields including some medical imaging, such as X ray computer tomography (X-CT), positron emission tomography (PET) and nuclear magnetic resonance imaging (MRI) etc, but the reconstructed effects are still not satisfied because original projection data are inevitably polluted by noises in process of image reconstruction. Although some traditional filters e.g., Shepp-Logan (SL) and Ram-Lak (RL) filter have the ability to filter some noises, Gibbs oscillation phenomenon are generated and artifacts leaded by back-projection are not greatly improved. Wavelet threshold denoising can overcome the noises interference to image reconstruction. Since some inherent defects exist in the traditional soft and hard threshold functions, an improved wavelet threshold function combined with filtered back-projection (FBP) algorithm was proposed in this paper. Four different reconstruction algorithms were compared in simulated experiments. Experimental results demonstrated that this improved algorithm greatly eliminated the shortcomings of un-continuity and large distortion of traditional threshold functions and the Gibbs oscillation. Finally, the availability of this improved algorithm was verified from the comparison of two evaluation criterions, i.e. mean square error (MSE), peak signal to noise ratio (PSNR) among four different algorithms, and the optimum dual threshold values of improved wavelet threshold function was gotten.

  13. Wavelet transform and Huffman coding based electrocardiogram compression algorithm: Application to telecardiology

    NASA Astrophysics Data System (ADS)

    Chouakri, S. A.; Djaafri, O.; Taleb-Ahmed, A.

    2013-08-01

    We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.

  14. Peak finding using biorthogonal wavelets

    SciTech Connect

    Tan, C.Y.

    2000-02-01

    The authors show in this paper how they can find the peaks in the input data if the underlying signal is a sum of Lorentzians. In order to project the data into a space of Lorentzian like functions, they show explicitly the construction of scaling functions which look like Lorentzians. From this construction, they can calculate the biorthogonal filter coefficients for both the analysis and synthesis functions. They then compare their biorthogonal wavelets to the FBI (Federal Bureau of Investigations) wavelets when used for peak finding in noisy data. They will show that in this instance, their filters perform much better than the FBI wavelets.

  15. Why are wavelets so effective

    SciTech Connect

    Resnikoff, H.L. )

    1993-01-01

    The theory of compactly supported wavelets is now 4 yr old. In that short period, it has stimulated significant research in pure mathematics; has been the source of new numerical methods for the solution of nonlinear partial differential equations, including Navier-Stokes; and has been applied to digital signal-processing problems, ranging from signal detection and classification to signal compression for speech, audio, images, seismic signals, and sonar. Wavelet channel coding has even been proposed for code division multiple access digital telephony. In each of these applications, prototype wavelet solutions have proved to be competitive with established methods, and in many cases they are already superior.

  16. Denoised and texture enhanced MVCT to improve soft tissue conspicuity

    SciTech Connect

    Sheng, Ke Qi, Sharon X.; Gou, Shuiping; Wu, Jiaolong

    2014-10-15

    Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT). Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast-to-noise ratios (CNRs) were quantified. Results: By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult-to-see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions-of-interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion

  17. BOOK REVIEW: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

    NASA Astrophysics Data System (ADS)

    Ng, J.; Kingsbury, N. G.

    2004-02-01

    wavelet. The second half of the chapter groups together miscellaneous points about the discrete wavelet transform, including coefficient manipulation for signal denoising and smoothing, a description of Daubechies’ wavelets, the properties of translation invariance and biorthogonality, the two-dimensional discrete wavelet transforms and wavelet packets. The fourth chapter is dedicated to wavelet transform methods in the author’s own specialty, fluid mechanics. Beginning with a definition of wavelet-based statistical measures for turbulence, the text proceeds to describe wavelet thresholding in the analysis of fluid flows. The remainder of the chapter describes wavelet analysis of engineering flows, in particular jets, wakes, turbulence and coherent structures, and geophysical flows, including atmospheric and oceanic processes. The fifth chapter describes the application of wavelet methods in various branches of engineering, including machining, materials, dynamics and information engineering. Unlike previous chapters, this (and subsequent) chapters are styled more as literature reviews that describe the findings of other authors. The areas addressed in this chapter include: the monitoring of machining processes, the monitoring of rotating machinery, dynamical systems, chaotic systems, non-destructive testing, surface characterization and data compression. The sixth chapter continues in this vein with the attention now turned to wavelets in the analysis of medical signals. Most of the chapter is devoted to the analysis of one-dimensional signals (electrocardiogram, neural waveforms, acoustic signals etc.), although there is a small section on the analysis of two-dimensional medical images. The seventh and final chapter of the book focuses on the application of wavelets in three seemingly unrelated application areas: fractals, finance and geophysics. The treatment on wavelet methods in fractals focuses on stochastic fractals with a short section on multifractals. The

  18. Wavelet entropy of stochastic processes

    NASA Astrophysics Data System (ADS)

    Zunino, L.; Pérez, D. G.; Garavaglia, M.; Rosso, O. A.

    2007-06-01

    We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the normalized total wavelet entropy (NTWS) family [S. Blanco, A. Figliola, R.Q. Quiroga, O.A. Rosso, E. Serrano, Time-frequency analysis of electroencephalogram series, III. Wavelet packets and information cost function, Phys. Rev. E 57 (1998) 932-940; O.A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Başar, Wavelet entropy: a new tool for analysis of short duration brain electrical signals, J. Neurosci. Method 105 (2001) 65-75] and a second introduced by Tavares and Lucena [Physica A 357(1) (2005) 71-78]. In order to understand their advantages and disadvantages, exact results obtained for fractional Gaussian noise ( -1<α< 1) and fractional Brownian motion ( 1<α< 3) are assessed. We find out that the NTWS family performs better as a characterization method for these stochastic processes.

  19. Wavelet theory and its applications

    SciTech Connect

    Faber, V.; Bradley, JJ.; Brislawn, C.; Dougherty, R.; Hawrylycz, M.

    1996-07-01

    This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). We investigated the theory of wavelet transforms and their relation to Laboratory applications. The investigators have had considerable success in the past applying wavelet techniques to the numerical solution of optimal control problems for distributed- parameter systems, nonlinear signal estimation, and compression of digital imagery and multidimensional data. Wavelet theory involves ideas from the fields of harmonic analysis, numerical linear algebra, digital signal processing, approximation theory, and numerical analysis, and the new computational tools arising from wavelet theory are proving to be ideal for many Laboratory applications. 10 refs.

  20. Rejuvenation of the skin surface: chemical peel and dermabrasion.

    PubMed

    Branham, G H; Thomas, J R

    1996-04-01

    Chemical peel and dermabrasion are traditional, well-proven methods for the rejuvenation of the skin. The medium-depth trichloroacetic acid peel and the deep phenol peel offer distinct advantages and disadvantages and are discussed in detail in this article. The management of complications associated with both peel techniques is also discussed. Regional dermabrasion is an effective adjunct to facial rejuvenative surgery, such as face lift and blepharoplasty. Full-face dermabrasion and spot or local dermabrasion are most often used in the treatment of facial scarring. The technique of dermabrasion is discussed as well as its indications and postoperative care. Results are shown for both dermabrasion and peel. PMID:9220727

  1. Apple peels as a value-added food ingredient.

    PubMed

    Wolfe, Kelly L; Liu, Rui Hai

    2003-03-12

    There is some evidence that chronic diseases, such as cancer and cardiovascular disease, may occur as a result of oxidative stress. Apple peels have high concentrations of phenolic compounds and may assist in the prevention of chronic diseases. Millions of pounds of waste apple peels are generated in the production of applesauce and canned apples in New York State each year. We proposed that a valuable food ingredient could be made using the peels of these apples if they could be dried and ground to a powder without large losses of phytochemicals. Rome Beauty apple peels were treated with citric acid dips, ascorbic acid dips, and blanches before being oven-dried at 60 degrees C. Only blanching treatments greatly preserved the phenolic compounds, and peels blanched for 10 s had the highest total phenolic content. Rome Beauty apple peels were then blanched for 10 s and dried under various conditions (oven-dried at 40, 60, or 80 degrees C, air-dried, or freeze-dried). The air-dried and freeze-dried apple peels had the highest total phenolic, flavonoid, and anthocyanin contents. On a fresh weight basis, the total phenolic and flavonoid contents of these samples were similar to those of the fresh apple peels. Freeze-dried peels had a lower water activity than air-dried peels on a fresh weight basis. The optimal processing conditions for the ingredient were blanching for 10s and freeze-drying. The process was scaled up, and the apple peel powder ingredient was characterized. The total phenolic content was 3342 +/- 12 mg gallic acid equivalents/100 g dried peels, the flavonoid content was 2299 +/- 52 mg catechin equivalents/100 g dried peels, and the anthocyanin content was 169.7 +/- 1.6 mg cyanidin 3-glucoside equivalents/100 g dried peels. These phytochemical contents were a significantly higher than those of the fresh apple peels if calculated on a fresh weight basis (p < 0.05). The apple peel powder had a total antioxidant activity of 1251 +/- 56 micromol vitamin C

  2. Wavelet multiscale processing of remote sensing data

    NASA Astrophysics Data System (ADS)

    Bagmanov, Valeriy H.; Kharitonov, Svyatoslav V.; Meshkov, Ivan K.; Sultanov, Albert H.

    2008-12-01

    There is comparative analysis of methods for estimation and definition of Hoerst index (index of self-similarity) and comparative analysis of wavelet types using for image decomposition are offered. Five types of compared wavelets are used for analysis: Haar wavelets, Daubechies wavelets, Discrete Meyer wavelets, symplets and coiflets. Best quality of restored image Meyer and Haar wavelets demonstrate, because of they are characterised by minimal errors of recomposition. But compression index for these types smaller, than for Daubechies wavelets, symplets and coiflets. Contrariwise the latter obtain less precision of decompression. As it is necessary to take into consideration the complexity of realization some wavelet transformation on digital signal processors (DSP), simplest method is Haar wavelet transformation.

  3. Extraction of phenolics from pomegranate peels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The effects of different solvents, temperature conditions, solvent-solid ratios and particle sizes on solid-solvent extraction of the total phenolics, proanthocyanidins and flavonoids herein also referred to as antioxidant from pomegranate marc peel (PMP) was studied. Water, methanol, ethanol, aceto...

  4. An Ap"peel"ing Activity

    ERIC Educational Resources Information Center

    Urich, Joshua A.; Sasse, Elizabeth A.

    2011-01-01

    This article describes a hands-on mathematics activity wherein students peel oranges to explore the surface area and volume of a sphere. This activity encourages students to make conjectures and hold mathematical discussions with both their peers and their teacher. Moreover, students develop formulas for the surface area and volume of a sphere…

  5. Chemical Peels for Melasma in Dark-Skinned Patients

    PubMed Central

    Sarkar, Rashmi; Bansal, Shuchi; Garg, Vijay K

    2012-01-01

    Melasma is a common disorder of hyperpigmentation, which has a severe impact on the quality of life. Inspite of tremendous research, the treatment remains frustrating both to the patient and the treating physician. Dark skin types (Fitzpatrick types IV to VI) are especially difficult to treat owing to the increased risk of post-inflammatory hyperpigmentation (PIH). The treatment ranges from a variety of easily applied topical therapies to agents like lasers and chemical peels. Peels are a well-known modality of treatment for melasma, having shown promising results in many clinical trials. However, in darker races, the choice of the peeling agent becomes relatively limited; so, there is the need for priming agents and additional maintenance peels. Although a number of new agents have come up, there is little published evidence supporting their use in day-to -day practice. The traditional glycolic peels prove to be the best both in terms of safety as well as efficacy. Lactic acid peels being relatively inexpensive and having shown equally good results in a few studies, definitely need further experimentation. We also recommend the use of a new peeling agent, the easy phytic solution, which does not require neutralisation unlike the traditional alpha-hydroxy peels. The choice of peeling agent, the peel concentration as well as the frequency and duration of peels are all important to achieve optimum results. PMID:23378706

  6. Wavelet-based polarimetry analysis

    NASA Astrophysics Data System (ADS)

    Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

    2014-06-01

    Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

  7. Low-Oscillation Complex Wavelets

    NASA Astrophysics Data System (ADS)

    ADDISON, P. S.; WATSON, J. N.; FENG, T.

    2002-07-01

    In this paper we explore the use of two low-oscillation complex wavelets—Mexican hat and Morlet—as powerful feature detection tools for data analysis. These wavelets, which have been largely ignored to date in the scientific literature, allow for a decomposition which is more “temporal than spectral” in wavelet space. This is shown to be useful for the detection of small amplitude, short duration signal features which are masked by much larger fluctuations. Wavelet transform-based methods employing these wavelets (based on both wavelet ridges and modulus maxima) are developed and applied to sonic echo NDT signals used for the analysis of structural elements. A new mobility scalogram and associated reflectogram is defined for analysis of impulse response characteristics of structural elements and a novel signal compression technique is described in which the pertinent signal information is contained within a few modulus maxima coefficients. As an example of its usefulness, the signal compression method is employed as a pre-processor for a neural network classifier. The authors believe that low oscillation complex wavelets have wide applicability to other practical signal analysis problems. Their possible application to two such problems is discussed briefly—the interrogation of arrhythmic ECG signals and the detection and characterization of coherent structures in turbulent flow fields.

  8. Tests for Wavelets as a Basis Set

    NASA Astrophysics Data System (ADS)

    Baker, Thomas; Evenbly, Glen; White, Steven

    A wavelet transformation is a special type of filter usually reserved for image processing and other applications. We develop metrics to evaluate wavelets for general problems on test one-dimensional systems. The goal is to eventually use a wavelet basis in electronic structure calculations. We compare a variety of orthogonal wavelets such as coiflets, symlets, and daubechies wavelets. We also evaluate a new type of orthogonal wavelet with dilation factor three which is both symmetric and compact in real space. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award #DE-SC008696.

  9. Singularity detection by wavelet approach: application to electrocardiogram signal

    NASA Astrophysics Data System (ADS)

    Jalil, Bushra; Beya, Ouadi; Fauvet, Eric; Laligant, Olivier

    2010-01-01

    In signal processing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database; a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.

  10. Blind source separation based x-ray image denoising from an image sequence.

    PubMed

    Yu, Chun-Yu; Li, Yan; Fei, Bin; Li, Wei-Liang

    2015-09-01

    Blind source separation (BSS) based x-ray image denoising from an image sequence is proposed. Without priori knowledge, the useful image signal can be separated from an x-ray image sequence, for original images are supposed as different combinations of stable image signal and random image noise. The BSS algorithms such as fixed-point independent component analysis and second-order statistics singular value decomposition are used and compared with multi-frame averaging which is a common algorithm for improving image's signal-to-noise ratio (SNR). Denoising performance is evaluated in SNR, standard deviation, entropy, and runtime. Analysis indicates that BSS is applicable to image denoising; the denoised image's quality will get better when more frames are included in an x-ray image sequence, but it will cost more time; there should be trade-off between denoising performance and runtime, which means that the number of frames included in an image sequence is enough. PMID:26429442

  11. Blind source separation based x-ray image denoising from an image sequence

    NASA Astrophysics Data System (ADS)

    Yu, Chun-Yu; Li, Yan; Fei, Bin; Li, Wei-Liang

    2015-09-01

    Blind source separation (BSS) based x-ray image denoising from an image sequence is proposed. Without priori knowledge, the useful image signal can be separated from an x-ray image sequence, for original images are supposed as different combinations of stable image signal and random image noise. The BSS algorithms such as fixed-point independent component analysis and second-order statistics singular value decomposition are used and compared with multi-frame averaging which is a common algorithm for improving image's signal-to-noise ratio (SNR). Denoising performance is evaluated in SNR, standard deviation, entropy, and runtime. Analysis indicates that BSS is applicable to image denoising; the denoised image's quality will get better when more frames are included in an x-ray image sequence, but it will cost more time; there should be trade-off between denoising performance and runtime, which means that the number of frames included in an image sequence is enough.

  12. Phase-aware candidate selection for time-of-flight depth map denoising

    NASA Astrophysics Data System (ADS)

    Hach, Thomas; Seybold, Tamara; Böttcher, Hendrik

    2015-03-01

    This paper presents a new pre-processing algorithm for Time-of-Flight (TOF) depth map denoising. Typically, denoising algorithms use the raw depth map as it comes from the sensor. Systematic artifacts due to the measurement principle are not taken into account which degrades the denoising results. For phase measurement TOF sensing, a major artifact is observed as salt-and-pepper noise caused by the measurement's ambiguity. Our pre-processing algorithm is able to isolate and unwrap affected pixels deploying the physical behavior of the capturing system yielding Gaussian noise. Using this pre-processing method before applying the denoising step clearly improves the parameter estimation for the denoising filter together with its final results.

  13. A New Method for Nonlocal Means Image Denoising Using Multiple Images

    PubMed Central

    Wang, Xingzheng; Wang, Haoqian; Yang, Jiangfeng; Zhang, Yongbing

    2016-01-01

    The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided. PMID:27459293

  14. Patch-ordering-based wavelet frame and its use in inverse problems.

    PubMed

    Ram, Idan; Cohen, Israel; Elad, Michael

    2014-07-01

    In our previous work [1] we have introduced a redundant tree-based wavelet transform (RTBWT), originally designed to represent functions defined on high dimensional data clouds and graphs. We have further shown that RTBWT can be used as a highly effective image-adaptive redundant transform that operates on an image using orderings of its overlapped patches. The resulting transform is robust to corruptions in the image, and thus able to efficiently represent the unknown target image even when it is calculated from its corrupted version. In this paper, we utilize this redundant transform as a powerful sparsity-promoting regularizer in inverse problems in image processing. We show that the image representation obtained with this transform is a frame expansion, and derive the analysis and synthesis operators associated with it. We explore the use of this frame operators to image denoising and deblurring, and demonstrate in both these cases state-of-the-art results.

  15. Research on power-law acoustic transient signal detection based on wavelet transform

    NASA Astrophysics Data System (ADS)

    Han, Jian-hui; Yang, Ri-jie; Wang, Wei

    2007-11-01

    Aiming at the characteristics of acoustic transient signal emitted from antisubmarine weapon which is being dropped into water (torpedo, aerial sonobuoy and rocket assisted depth charge etc.), such as short duration, low SNR, abruptness and instability, based on traditional power-law detector, a new method to detect acoustic transient signal is proposed. Firstly wavelet transform is used to de-noise signal, removes random spectrum components and improves SNR. Then Power- Law detector is adopted to detect transient signal. The simulation results show the method can effectively extract envelop characteristic of transient signal on the condition of low SNR. The performance of WT-Power-Law markedly outgoes that of traditional Power-Law detection method.

  16. Prediction of total volatile basic nitrogen contents using wavelet features from visible/near-infrared hyperspectral images of prawn (Metapenaeus ensis).

    PubMed

    Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zhu, Zhiwei; Pu, Hongbin

    2016-04-15

    A visible/near-infrared hyperspectral imaging (HSI) system (400-1000 nm) coupled with wavelet analysis was used to determine the total volatile basic nitrogen (TVB-N) contents of prawns during cold storage. Spectral information was denoised by conducting wavelet analysis and uninformative variable elimination (UVE) algorithm, and then three wavelet features (energy, entropy and modulus maxima) were extracted. Quantitative models were established between the wavelet features and the reference TVB-N contents by using three regression algorithms. As a result, the LS-SVM model with modulus maxima features was considered as the best model for determining the TVB-N contents of prawns, with an excellent RP(2) of 0.9547, RMSEP=0.7213 mg N/100g and RPD=4.799. Finally, an image processing algorithm was developed for generating a TVB-N distribution map. This study demonstrated the possibility of applying the HSI imaging system in combination with wavelet analysis to the monitoring of TVB-N values in prawns. PMID:26616948

  17. A novel method for image denoising of fluorescence molecular imaging based on fuzzy C-Means clustering

    NASA Astrophysics Data System (ADS)

    An, Yu; Liu, Jie; Ye, Jinzuo; Mao, Yamin; Yang, Xin; Jiang, Shixin; Chi, Chongwei; Tian, Jie

    2015-03-01

    As an important molecular imaging modality, fluorescence molecular imaging (FMI) has the advantages of high sensitivity, low cost and ease of use. By labeling the regions of interest with fluorophore, FMI can noninvasively obtain the distribution of fluorophore in-vivo. However, due to the fact that the spectrum of fluorescence is in the section of the visible light range, there are mass of autofluorescence on the surface of the bio-tissues, which is a major disturbing factor in FMI. Meanwhile, the high-level of dark current for charge-coupled device (CCD) camera and other influencing factor can also produce a lot of background noise. In this paper, a novel method for image denoising of FMI based on fuzzy C-Means clustering (FCM) is proposed, because the fluorescent signal is the major component of the fluorescence images, and the intensity of autofluorescence and other background signals is relatively lower than the fluorescence signal. First, the fluorescence image is smoothed by sliding-neighborhood operations to initially eliminate the noise. Then, the wavelet transform (WLT) is performed on the fluorescence images to obtain the major component of the fluorescent signals. After that, the FCM method is adopt to separate the major component and background of the fluorescence images. Finally, the proposed method was validated using the original data obtained by in vivo implanted fluorophore experiment, and the results show that our proposed method can effectively obtain the fluorescence signal while eliminate the background noise, which could increase the quality of fluorescence images.

  18. A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

    NASA Astrophysics Data System (ADS)

    Karimi, Davood; Ward, Rabab K.

    2016-03-01

    Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

  19. PDE-based Non-Linear Diffusion Techniques for Denoising Scientific and Industrial Images: An Empirical Study

    SciTech Connect

    Weeratunga, S K; Kamath, C

    2001-12-20

    Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, they focus on one such approach, namely the use of non-linear diffusion operators. This approach has been studied extensively from a theoretical viewpoint ever since the 1987 work of Perona and Malik showed that non-linear filters outperformed the more traditional linear Canny edge detector. They complement this theoretical work by investigating the performance of several isotropic diffusion operators on test images from scientific domains. They explore the effects of various parameters such as the choice of diffusivity function, explicit and implicit methods for the discretization of the PDE, and approaches for the spatial discretization of the non-linear operator etc. They also compare these schemes with simple spatial filters and the more complex wavelet-based shrinkage techniques. The empirical results show that, with an appropriate choice of parameters, diffusion-based schemes can be as effective as competitive techniques.

  20. Inconsistent Denoising and Clustering Algorithms for Amplicon Sequence Data.

    PubMed

    Koskinen, Kaisa; Auvinen, Petri; Björkroth, K Johanna; Hultman, Jenni

    2015-08-01

    Natural microbial communities have been studied for decades using the 16S rRNA gene as a marker. In recent years, the application of second-generation sequencing technologies has revolutionized our understanding of the structure and function of microbial communities in complex environments. Using these highly parallel techniques, a detailed description of community characteristics are constructed, and even the rare biosphere can be detected. The new approaches carry numerous advantages and lack many features that skewed the results using traditional techniques, but we are still facing serious bias, and the lack of reliable comparability of produced results. Here, we contrasted publicly available amplicon sequence data analysis algorithms by using two different data sets, one with defined clone-based structure, and one with food spoilage community with well-studied communities. We aimed to assess which software and parameters produce results that resemble the benchmark community best, how large differences can be detected between methods, and whether these differences are statistically significant. The results suggest that commonly accepted denoising and clustering methods used in different combinations produce significantly different outcome: clustering method impacts greatly on the number of operational taxonomic units (OTUs) and denoising algorithm influences more on taxonomic affiliations. The magnitude of the OTU number difference was up to 40-fold and the disparity between results seemed highly dependent on the community structure and diversity. Statistically significant differences in taxonomies between methods were seen even at phylum level. However, the application of effective denoising method seemed to even out the differences produced by clustering. PMID:25525895

  1. Group-normalized wavelet packet signal processing

    NASA Astrophysics Data System (ADS)

    Shi, Zhuoer; Bao, Zheng

    1997-04-01

    Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, group- normalized wavelet packet transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)-frequency masking technique. The extended F-entropy improves the performance of GNWPT. For perception-based image, soft-logic masking is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signal processing, such as images and ECG signal, the compactly supported spline or bi- orthogonal wavelet packets are preferred for perfect de- noising and filtering qualities.

  2. A Mellin transform approach to wavelet analysis

    NASA Astrophysics Data System (ADS)

    Alotta, Gioacchino; Di Paola, Mario; Failla, Giuseppe

    2015-11-01

    The paper proposes a fractional calculus approach to continuous wavelet analysis. Upon introducing a Mellin transform expression of the mother wavelet, it is shown that the wavelet transform of an arbitrary function f(t) can be given a fractional representation involving a suitable number of Riesz integrals of f(t), and corresponding fractional moments of the mother wavelet. This result serves as a basis for an original approach to wavelet analysis of linear systems under arbitrary excitations. In particular, using the proposed fractional representation for the wavelet transform of the excitation, it is found that the wavelet transform of the response can readily be computed by a Mellin transform expression, with fractional moments obtained from a set of algebraic equations whose coefficient matrix applies for any scale a of the wavelet transform. Robustness and computationally efficiency of the proposed approach are shown in the paper.

  3. [Application of a modified method of wavelet noise removing to noisy ICP-AES spectra].

    PubMed

    Ma, Xiao-guo; Zhang, Zhan-xia

    2003-06-01

    A new method for noise removal from signal by the wavelet transform was developed. Compared with analytical signal, noise has higher frequency and smaller amplitude. By the new wavelet filtering method, the high frequency components were first removed, and then the small ones in the remaining transformed vectors were discarded. The proposed approach was evaluated by the processing of simulated and experimental noisy ICP-AES spectra. Different amounts of noise were added to a Gaussian peak to obtain a series of noisy ICP spectra. The simulated noisy spectra with R (signal to noise ratio) = 6 and N (data number) = 51, and with R = 6 and N = 17 were used to illustrate the feasibility of the proposed method. The performances of noise removal by the wavelet smoothing, the wavelet denoising and the proposed technique were compared. It was found that using the new approach, the relative errors of peak height would be no more than 5% for spectra with normal sampling points and R > or = 2. Moreover, the baseline could be easily defined, which was helpful to the accurate measurement of peak height. Experimental spectra of Al and V at low concentrations were processed by the proposed method. Intense noises were efficiently removed and the spectra became smoother without underestimating the analytical signal. The distortion of V 303.310 nm line was substantially rectified. The linear correlation coefficients between the peak heights in the reconstructed spectra and the concentrations were found to be 0.9953 for Al and 0.9836 for V, respectively. PMID:12953539

  4. Adaptive Multilinear Tensor Product Wavelets.

    PubMed

    Weiss, Kenneth; Lindstrom, Peter

    2016-01-01

    Many foundational visualization techniques including isosurfacing, direct volume rendering and texture mapping rely on piecewise multilinear interpolation over the cells of a mesh. However, there has not been much focus within the visualization community on techniques that efficiently generate and encode globally continuous functions defined by the union of multilinear cells. Wavelets provide a rich context for analyzing and processing complicated datasets. In this paper, we exploit adaptive regular refinement as a means of representing and evaluating functions described by a subset of their nonzero wavelet coefficients. We analyze the dependencies involved in the wavelet transform and describe how to generate and represent the coarsest adaptive mesh with nodal function values such that the inverse wavelet transform is exactly reproduced via simple interpolation (subdivision) over the mesh elements. This allows for an adaptive, sparse representation of the function with on-demand evaluation at any point in the domain. We focus on the popular wavelets formed by tensor products of linear B-splines, resulting in an adaptive, nonconforming but crack-free quadtree (2D) or octree (3D) mesh that allows reproducing globally continuous functions via multilinear interpolation over its cells.

  5. Wavelet-based Evapotranspiration Forecasts

    NASA Astrophysics Data System (ADS)

    Bachour, R.; Maslova, I.; Ticlavilca, A. M.; McKee, M.; Walker, W.

    2012-12-01

    Providing a reliable short-term forecast of evapotranspiration (ET) could be a valuable element for improving the efficiency of irrigation water delivery systems. In the last decade, wavelet transform has become a useful technique for analyzing the frequency domain of hydrological time series. This study shows how wavelet transform can be used to access statistical properties of evapotranspiration. The objective of the research reported here is to use wavelet-based techniques to forecast ET up to 16 days ahead, which corresponds to the LANDSAT 7 overpass cycle. The properties of the ET time series, both physical and statistical, are examined in the time and frequency domains. We use the information about the energy decomposition in the wavelet domain to extract meaningful components that are used as inputs for ET forecasting models. Seasonal autoregressive integrated moving average (SARIMA) and multivariate relevance vector machine (MVRVM) models are coupled with the wavelet-based multiresolution analysis (MRA) results and used to generate short-term ET forecasts. Accuracy of the models is estimated and model robustness is evaluated using the bootstrap approach.

  6. A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

    NASA Astrophysics Data System (ADS)

    Rubel, Aleksey S.; Lukin, Vladimir V.; Egiazarian, Karen O.

    2015-03-01

    Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

  7. The role of peel stresses in cyclic debonding

    NASA Technical Reports Server (NTRS)

    Everett, R. A., Jr.

    1982-01-01

    When an adhesively bonded joint is undergoing cyclic loading, one of the possible damage modes that occurs is called cyclic debonding - progressive separation of the adherends by failure of the adhesive bond under cyclic loading. In most practical structures, both peel and shear stresses exist in the adhesive bonding during cyclic loading. The results of an experimental and analytical study to determine the role of peel stresses on cyclic debonding in a mixed mode specimen are presented. Experimentally, this was done by controlling the forces that create the peel stresses by applying a clamping force to oppose the peel stresses. Cracked lap shear joints were chosen for this study. A finite element analysis was developed to assess the effect of the clamping force on the strain energy release rates due to shear and peel stresses. The results imply that the peel stress is the principal stress causing cyclic debonding.

  8. Wavelet Representation of Contour Sets

    SciTech Connect

    Bertram, M; Laney, D E; Duchaineau, M A; Hansen, C D; Hamann, B; Joy, K I

    2001-07-19

    We present a new wavelet compression and multiresolution modeling approach for sets of contours (level sets). In contrast to previous wavelet schemes, our algorithm creates a parametrization of a scalar field induced by its contoum and compactly stores this parametrization rather than function values sampled on a regular grid. Our representation is based on hierarchical polygon meshes with subdivision connectivity whose vertices are transformed into wavelet coefficients. From this sparse set of coefficients, every set of contours can be efficiently reconstructed at multiple levels of resolution. When applying lossy compression, introducing high quantization errors, our method preserves contour topology, in contrast to compression methods applied to the corresponding field function. We provide numerical results for scalar fields defined on planar domains. Our approach generalizes to volumetric domains, time-varying contours, and level sets of vector fields.

  9. Wavelets for sign language translation

    NASA Astrophysics Data System (ADS)

    Wilson, Beth J.; Anspach, Gretel

    1993-10-01

    Wavelet techniques are applied to help extract the relevant parameters of sign language from video images of a person communicating in American Sign Language or Signed English. The compression and edge detection features of two-dimensional wavelet analysis are exploited to enhance the algorithms under development to classify the hand motion, hand location with respect to the body, and handshape. These three parameters have different processing requirements and complexity issues. The results are described for applying various quadrature mirror filter designs to a filterbank implementation of the desired wavelet transform. The overall project is to develop a system that will translate sign language to English to facilitate communication between deaf and hearing people.

  10. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

    PubMed Central

    He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

    2014-01-01

    Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045

  11. Parameter optimization for image denoising based on block matching and 3D collaborative filtering

    NASA Astrophysics Data System (ADS)

    Pedada, Ramu; Kugu, Emin; Li, Jiang; Yue, Zhanfeng; Shen, Yuzhong

    2009-02-01

    Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation.

  12. Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis.

    PubMed

    He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

    2013-12-27

    Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

  13. Extraction of bromelain from pineapple peels.

    PubMed

    Ketnawa, S; Chaiwut, P; Rawdkuen, S

    2011-08-01

    Large amount of pineapple peels (by-products) is left over after processing and they are a potential source for bromelain extraction. Distilled water (DI), DI containing cysteine and ethylenediaminetetraacetic acid (EDTA) (DI-CE), sodium phosphate buffer pH 7.0 (PB) and PB containing cysteine and EDTA (PB-CE) were used as extractants for bromelain from the pineapple peels. The highest bromelain activity was obtained when it was extracted with PB-CE (867 and 1032 units for Nang Lae and Phu Lae cultv, respectively). The PB could maintain the pH of the extract (pH 5.1-5.7) when compared with others. Under sodium dodecyl sulfate polyacrylamide gel electrophoresis, the extract showed protein bands in the range 24-28 kDa. The protein band with a molecular weight of ∼28 kDa exposed the clear zone on blue background under the casein-substrate gel electrophoresis. The effects of the bromelain extract on the protein patterns of beef, chicken and squid muscles were also determined. Trichloroacetic acid soluble peptide content of all the treated muscles increased when the amount of bromelain extract increased. Decrease in myosin heavy chains and actin was observed in all the muscle types when bromelain extract was used. The best extractant for bromelain from pineapple peels was PB-CE. Moreover, bromelain extract could be used as a muscle food tenderizing agent in food industries. PMID:21813595

  14. Extraction of bromelain from pineapple peels.

    PubMed

    Ketnawa, S; Chaiwut, P; Rawdkuen, S

    2011-08-01

    Large amount of pineapple peels (by-products) is left over after processing and they are a potential source for bromelain extraction. Distilled water (DI), DI containing cysteine and ethylenediaminetetraacetic acid (EDTA) (DI-CE), sodium phosphate buffer pH 7.0 (PB) and PB containing cysteine and EDTA (PB-CE) were used as extractants for bromelain from the pineapple peels. The highest bromelain activity was obtained when it was extracted with PB-CE (867 and 1032 units for Nang Lae and Phu Lae cultv, respectively). The PB could maintain the pH of the extract (pH 5.1-5.7) when compared with others. Under sodium dodecyl sulfate polyacrylamide gel electrophoresis, the extract showed protein bands in the range 24-28 kDa. The protein band with a molecular weight of ∼28 kDa exposed the clear zone on blue background under the casein-substrate gel electrophoresis. The effects of the bromelain extract on the protein patterns of beef, chicken and squid muscles were also determined. Trichloroacetic acid soluble peptide content of all the treated muscles increased when the amount of bromelain extract increased. Decrease in myosin heavy chains and actin was observed in all the muscle types when bromelain extract was used. The best extractant for bromelain from pineapple peels was PB-CE. Moreover, bromelain extract could be used as a muscle food tenderizing agent in food industries.

  15. Denoising Stimulated Raman Spectroscopic Images by Total Variation Minimization

    PubMed Central

    Liao, Chien-Sheng; Choi, Joon Hee; Zhang, Delong; Chan, Stanley H.; Cheng, Ji-Xin

    2016-01-01

    High-speed coherent Raman scattering imaging is opening a new avenue to unveiling the cellular machinery by visualizing the spatio-temporal dynamics of target molecules or intracellular organelles. By extracting signals from the laser at MHz modulation frequency, current stimulated Raman scattering (SRS) microscopy has reached shot noise limited detection sensitivity. The laser-based local oscillator in SRS microscopy not only generates high levels of signal, but also delivers a large shot noise which degrades image quality and spectral fidelity. Here, we demonstrate a denoising algorithm that removes the noise in both spatial and spectral domains by total variation minimization. The signal-to-noise ratio of SRS spectroscopic images was improved by up to 57 times for diluted dimethyl sulfoxide solutions and by 15 times for biological tissues. Weak Raman peaks of target molecules originally buried in the noise were unraveled. Coupling the denoising algorithm with multivariate curve resolution allowed discrimination of fat stores from protein-rich organelles in C. elegans. Together, our method significantly improved detection sensitivity without frame averaging, which can be useful for in vivo spectroscopic imaging. PMID:26955400

  16. Ladar range image denoising by a nonlocal probability statistics algorithm

    NASA Astrophysics Data System (ADS)

    Xia, Zhi-Wei; Li, Qi; Xiong, Zhi-Peng; Wang, Qi

    2013-01-01

    According to the characteristic of range images of coherent ladar and the basis of nonlocal means (NLM), a nonlocal probability statistics (NLPS) algorithm is proposed in this paper. The difference is that NLM performs denoising using the mean of the conditional probability distribution function (PDF) while NLPS using the maximum of the marginal PDF. In the algorithm, similar blocks are found out by the operation of block matching and form a group. Pixels in the group are analyzed by probability statistics and the gray value with maximum probability is used as the estimated value of the current pixel. The simulated range images of coherent ladar with different carrier-to-noise ratio and real range image of coherent ladar with 8 gray-scales are denoised by this algorithm, and the results are compared with those of median filter, multitemplate order mean filter, NLM, median nonlocal mean filter and its incorporation of anatomical side information, and unsupervised information-theoretic adaptive filter. The range abnormality noise and Gaussian noise in range image of coherent ladar are effectively suppressed by NLPS.

  17. Sparsity-based Poisson denoising with dictionary learning.

    PubMed

    Giryes, Raja; Elad, Michael

    2014-12-01

    The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging, and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive-independent identically distributed. Gaussian noise, for which many effective algorithms are available. However, in a low-SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. Salmon et al took this route, proposing a patch-based exponential image representation model based on Gaussian mixture model, leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping-based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR and achieving state-of-the-art results in cases of low SNR. PMID:25312930

  18. Noise distribution and denoising of current density images

    PubMed Central

    Beheshti, Mohammadali; Foomany, Farbod H.; Magtibay, Karl; Jaffray, David A.; Krishnan, Sridhar; Nanthakumar, Kumaraswamy; Umapathy, Karthikeyan

    2015-01-01

    Abstract. Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations. The outcome can be affected significantly, especially at a low signal-to-noise ratio (SNR). We have shown the residual noise distribution of the phase to be Gaussian-like and the noise in CDI images approximated as a Gaussian. This finding matches experimental results. We further investigated this finding by performing comparative analysis with denoising techniques, using two CDI datasets with two different currents (20 and 45 mA). We found that the block-matching and three-dimensional (BM3D) technique outperforms other techniques when applied on current density (J). The minimum gain in noise power by BM3D applied to J compared with the next best technique in the analysis was found to be around 2 dB per pixel. We characterize the noise profile in CDI images and provide insights on the performance of different denoising techniques when applied at two different stages of current density reconstruction. PMID:26158100

  19. Efficient bias correction for magnetic resonance image denoising.

    PubMed

    Mukherjee, Partha Sarathi; Qiu, Peihua

    2013-05-30

    Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications. PMID:23074149

  20. HARDI denoising using nonlocal means on S2

    NASA Astrophysics Data System (ADS)

    Kuurstra, Alan; Dolui, Sudipto; Michailovich, Oleg

    2012-02-01

    Diffusion MRI (dMRI) is a unique imaging modality for in vivo delineation of the anatomical structure of white matter in the brain. In particular, high angular resolution diffusion imaging (HARDI) is a specific instance of dMRI which is known to excel in detection of multiple neural fibers within a single voxel. Unfortunately, the angular resolution of HARDI is known to be inversely proportional to SNR, which makes the problem of denoising of HARDI data be of particular practical importance. Since HARDI signals are effectively band-limited, denoising can be accomplished by means of linear filtering. However, the spatial dependency of diffusivity in brain tissue makes it impossible to find a single set of linear filter parameters which is optimal for all types of diffusion signals. Hence, adaptive filtering is required. In this paper, we propose a new type of non-local means (NLM) filtering which possesses the required adaptivity property. As opposed to similar methods in the field, however, the proposed NLM filtering is applied in the spherical domain of spatial orientations. Moreover, the filter uses an original definition of adaptive weights, which are designed to be invariant to both spatial rotations as well as to a particular sampling scheme in use. As well, we provide a detailed description of the proposed filtering procedure, its efficient implementation, as well as experimental results with synthetic data. We demonstrate that our filter has substantially better adaptivity as compared to a number of alternative methods.

  1. The peel test in experimental adhesive fracture mechanics

    NASA Technical Reports Server (NTRS)

    Anderson, G. P.; Devries, K. L.; Williams, M. L.

    1974-01-01

    Several testing methods have been proposed for obtaining critical energy release rate or adhesive fracture energy in bond systems. These tests include blister, cone, lap shear, and peel tests. Peel tests have been used for many years to compare relative strengths of different adhesives, different surface preparation techniques, etc. The present work demonstrates the potential use of the peel test for obtaining adhesive fracture energy values.

  2. Recent advances in wavelet technology

    NASA Technical Reports Server (NTRS)

    Wells, R. O., Jr.

    1994-01-01

    Wavelet research has been developing rapidly over the past five years, and in particular in the academic world there has been significant activity at numerous universities. In the industrial world, there has been developments at Aware, Inc., Lockheed, Martin-Marietta, TRW, Kodak, Exxon, and many others. The government agencies supporting wavelet research and development include ARPA, ONR, AFOSR, NASA, and many other agencies. The recent literature in the past five years includes a recent book which is an index of citations in the past decade on this subject, and it contains over 1,000 references and abstracts.

  3. Adaptive wavelets and relativistic magnetohydrodynamics

    NASA Astrophysics Data System (ADS)

    Hirschmann, Eric; Neilsen, David; Anderson, Matthe; Debuhr, Jackson; Zhang, Bo

    2016-03-01

    We present a method for integrating the relativistic magnetohydrodynamics equations using iterated interpolating wavelets. Such provide an adaptive implementation for simulations in multidimensions. A measure of the local approximation error for the solution is provided by the wavelet coefficients. They place collocation points in locations naturally adapted to the flow while providing expected conservation. We present demanding 1D and 2D tests includingthe Kelvin-Helmholtz instability and the Rayleigh-Taylor instability. Finally, we consider an outgoing blast wave that models a GRB outflow.

  4. Automatic Denoising and Unmixing in Hyperspectral Image Processing

    NASA Astrophysics Data System (ADS)

    Peng, Honghong

    This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein

  5. Visibility of wavelet quantization noise

    NASA Technical Reports Server (NTRS)

    Watson, A. B.; Yang, G. Y.; Solomon, J. A.; Villasenor, J.

    1997-01-01

    The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

  6. Wavelet library for constrained devices

    NASA Astrophysics Data System (ADS)

    Ehlers, Johan Hendrik; Jassim, Sabah A.

    2007-04-01

    The wavelet transform is a powerful tool for image and video processing, useful in a range of applications. This paper is concerned with the efficiency of a certain fast-wavelet-transform (FWT) implementation and several wavelet filters, more suitable for constrained devices. Such constraints are typically found on mobile (cell) phones or personal digital assistants (PDA). These constraints can be a combination of; limited memory, slow floating point operations (compared to integer operations, most often as a result of no hardware support) and limited local storage. Yet these devices are burdened with demanding tasks such as processing a live video or audio signal through on-board capturing sensors. In this paper we present a new wavelet software library, HeatWave, that can be used efficiently for image/video processing/analysis tasks on mobile phones and PDA's. We will demonstrate that HeatWave is suitable for realtime applications with fine control and range to suit transform demands. We shall present experimental results to substantiate these claims. Finally this library is intended to be of real use and applied, hence we considered several well known and common embedded operating system platform differences; such as a lack of common routines or functions, stack limitations, etc. This makes HeatWave suitable for a range of applications and research projects.

  7. Interframe vector wavelet coding technique

    NASA Astrophysics Data System (ADS)

    Wus, John P.; Li, Weiping

    1997-01-01

    Wavelet coding is often used to divide an image into multi- resolution wavelet coefficients which are quantized and coded. By 'vectorizing' scalar wavelet coding and combining this with vector quantization (VQ), vector wavelet coding (VWC) can be implemented. Using a finite number of states, finite-state vector quantization (FSVQ) takes advantage of the similarity between frames by incorporating memory into the video coding system. Lattice VQ eliminates the potential mismatch that could occur using pre-trained VQ codebooks. It also eliminates the need for codebook storage in the VQ process, thereby creating a more robust coding system. Therefore, by using the VWC coding method in conjunction with the FSVQ system and lattice VQ, the formulation of a high quality very low bit rate coding systems is proposed. A coding system using a simple FSVQ system where the current state is determined by the previous channel symbol only is developed. To achieve a higher degree of compression, a tree-like FSVQ system is implemented. The groupings are done in this tree-like structure from the lower subbands to the higher subbands in order to exploit the nature of subband analysis in terms of the parent-child relationship. Class A and Class B video sequences from the MPEG-IV testing evaluations are used in the evaluation of this coding method.

  8. Wavelet Approximation in Data Assimilation

    NASA Technical Reports Server (NTRS)

    Tangborn, Andrew; Atlas, Robert (Technical Monitor)

    2002-01-01

    Estimation of the state of the atmosphere with the Kalman filter remains a distant goal because of high computational cost of evolving the error covariance for both linear and nonlinear systems. Wavelet approximation is presented here as a possible solution that efficiently compresses both global and local covariance information. We demonstrate the compression characteristics on the the error correlation field from a global two-dimensional chemical constituent assimilation, and implement an adaptive wavelet approximation scheme on the assimilation of the one-dimensional Burger's equation. In the former problem, we show that 99%, of the error correlation can be represented by just 3% of the wavelet coefficients, with good representation of localized features. In the Burger's equation assimilation, the discrete linearized equations (tangent linear model) and analysis covariance are projected onto a wavelet basis and truncated to just 6%, of the coefficients. A nearly optimal forecast is achieved and we show that errors due to truncation of the dynamics are no greater than the errors due to covariance truncation.

  9. Noise reduction of nuclear magnetic resonance (NMR) transversal data using improved wavelet transform and exponentially weighted moving average (EWMA)

    NASA Astrophysics Data System (ADS)

    Ge, Xinmin; Fan, Yiren; Li, Jiangtao; Wang, Yang; Deng, Shaogui

    2015-02-01

    NMR logging and core NMR signals acts as an effective way of pore structure evaluation and fluid discrimination, but it is greatly contaminated by noise for samples with low magnetic resonance intensity. Transversal relaxation time (T2) spectrum obtained by inversion of decay signals intrigued by Carr-Purcell-Meiboom-Gill (CPMG) sequence may deviate from the truth if the signal-to-noise ratio (SNR) is imperfect. A method of combing the improved wavelet thresholding with the EWMA is proposed for noise reduction of decay data. The wavelet basis function and decomposition level are optimized in consideration of information entropy and white noise estimation firstly. Then a hybrid threshold function is developed to avoid drawbacks of hard and soft threshold functions. To achieve the best thresholding values of different levels, a nonlinear objective function based on SNR and mean square error (MSE) is constructed, transforming the problem to a task of finding optimal solutions. Particle swarm optimization (PSO) is used to ensure the stability and global convergence. EWMA is carried out to eliminate unwanted peaks and sawtooths of the wavelet denoised signal. With validations of numerical simulations and experiments, it is demonstrated that the proposed approach can reduce the noise of T2 decay data perfectly.

  10. Wavelet analysis on vibration modal frequency measurement at a low level of strain of the turbine blade using FBG sensors

    NASA Astrophysics Data System (ADS)

    Huang, Xue-feng; Liu, Yan; Luo, Dan; Wang, Guan-qing; Ding, Ning; Xu, Jiang-rong; Huang, Xue-jing

    2010-01-01

    Vibration modal frequency measurement of a turbine blade was investigated by using fiber Bragg grating (FBG) sensors for their adaptation for a low level of strain at high frequency. However, the signal-to-noise ratio was so low that it was very difficult to identify dominant modal frequency from a raw signal acquired. An attempt was made to solve this problem. First, a bi-linear transform elliptic filter with pass-band and stop-band was proposed to remove electromagnetic interference noise. Second, discrete stationary wavelet transform with a soft threshold was utilized to de-noise the high-frequency part. Third, wavelet packet transform was exploited to decompose the time signal and to obtain the power spectrum and identification of dominant modal frequency. Experimental and analytic results demonstrated that four stages of dominant modal frequencies of the turbine blade without any constraint condition (free vibration) and three stages of dominant modal frequencies with a constraint condition (A-type vibration) were obtained, respectively. To testify to the validity of analytic results, a professional computer random signal and vibration analysis system (CRAS) was used to measure modal frequency. The results illustrated that modal frequencies obtained by the CRAS platform yielded close agreement with those based on FBG sensors. Obviously, wavelet analysis is successfully employed to decode vibration modal frequency from a raw signal of FBG sensors.

  11. Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.

    PubMed

    Athanasiadis, Emmanouil I; Cavouras, Dionisis A; Glotsos, Dimitris Th; Georgiadis, Pantelis V; Kalatzis, Ioannis K; Nikiforidis, George C

    2009-11-01

    A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r2 : 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively).

  12. Ocean Wave Separation Using CEEMD-Wavelet in GPS Wave Measurement.

    PubMed

    Wang, Junjie; He, Xiufeng; Ferreira, Vagner G

    2015-08-07

    Monitoring ocean waves plays a crucial role in, for example, coastal environmental and protection studies. Traditional methods for measuring ocean waves are based on ultrasonic sensors and accelerometers. However, the Global Positioning System (GPS) has been introduced recently and has the advantage of being smaller, less expensive, and not requiring calibration in comparison with the traditional methods. Therefore, for accurately measuring ocean waves using GPS, further research on the separation of the wave signals from the vertical GPS-mounted carrier displacements is still necessary. In order to contribute to this topic, we present a novel method that combines complementary ensemble empirical mode decomposition (CEEMD) with a wavelet threshold denoising model (i.e., CEEMD-Wavelet). This method seeks to extract wave signals with less residual noise and without losing useful information. Compared with the wave parameters derived from the moving average skill, high pass filter and wave gauge, the results show that the accuracy of the wave parameters for the proposed method was improved with errors of about 2 cm and 0.2 s for mean wave height and mean period, respectively, verifying the validity of the proposed method.

  13. Ocean Wave Separation Using CEEMD-Wavelet in GPS Wave Measurement

    PubMed Central

    Wang, Junjie; He, Xiufeng; Ferreira, Vagner G.

    2015-01-01

    Monitoring ocean waves plays a crucial role in, for example, coastal environmental and protection studies. Traditional methods for measuring ocean waves are based on ultrasonic sensors and accelerometers. However, the Global Positioning System (GPS) has been introduced recently and has the advantage of being smaller, less expensive, and not requiring calibration in comparison with the traditional methods. Therefore, for accurately measuring ocean waves using GPS, further research on the separation of the wave signals from the vertical GPS-mounted carrier displacements is still necessary. In order to contribute to this topic, we present a novel method that combines complementary ensemble empirical mode decomposition (CEEMD) with a wavelet threshold denoising model (i.e., CEEMD-Wavelet). This method seeks to extract wave signals with less residual noise and without losing useful information. Compared with the wave parameters derived from the moving average skill, high pass filter and wave gauge, the results show that the accuracy of the wave parameters for the proposed method was improved with errors of about 2 cm and 0.2 s for mean wave height and mean period, respectively, verifying the validity of the proposed method. PMID:26262620

  14. Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation.

    PubMed

    Bao, L J; Zhu, Y M; Liu, W Y; Croisille, P; Pu, Z B; Robini, M; Magnin, I E

    2009-03-21

    Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the image's features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures. PMID:19218737

  15. Locally Based Kernel PLS Regression De-noising with Application to Event-Related Potentials

    NASA Technical Reports Server (NTRS)

    Rosipal, Roman; Trejo, Leonard J.; Wheeler, Kevin; Tino, Peter

    2002-01-01

    The close relation of signal de-noising and regression problems dealing with the estimation of functions reflecting dependency between a set of inputs and dependent outputs corrupted with some level of noise have been employed in our approach.

  16. Exploiting the self-similarity in ERP images by nonlocal means for single-trial denoising.

    PubMed

    Strauss, Daniel J; Teuber, Tanja; Steidl, Gabriele; Corona-Strauss, Farah I

    2013-07-01

    Event related potentials (ERPs) represent a noninvasive and widely available means to analyze neural correlates of sensory and cognitive processing. Recent developments in neural and cognitive engineering proposed completely new application fields of this well-established measurement technique when using an advanced single-trial processing. We have recently shown that 2-D diffusion filtering methods from image processing can be used for the denoising of ERP single-trials in matrix representations, also called ERP images. In contrast to conventional 1-D transient ERP denoising techniques, the 2-D restoration of ERP images allows for an integration of regularities over multiple stimulations into the denoising process. Advanced anisotropic image restoration methods may require directional information for the ERP denoising process. This is especially true if there is a lack of a priori knowledge about possible traces in ERP images. However due to the use of event related experimental paradigms, ERP images are characterized by a high degree of self-similarity over the individual trials. In this paper, we propose the simple and easy to apply nonlocal means method for ERP image denoising in order to exploit this self-similarity rather than focusing on the edge-based extraction of directional information. Using measured and simulated ERP data, we compare our method to conventional approaches in ERP denoising. It is concluded that the self-similarity in ERP images can be exploited for single-trial ERP denoising by the proposed approach. This method might be promising for a variety of evoked and event-related potential applications, including nonstationary paradigms such as changing exogeneous stimulus characteristics or endogenous states during the experiment. As presented, the proposed approach is for the a posteriori denoising of single-trial sequences.

  17. Feasibility of Jujube peeling using novel infrared radiation heating technology

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Infrared (IR) radiation heating has a promising potential to be used as a sustainable and effective method to eliminate the use of water and chemicals in the jujube-peeling process and enhance the quality of peeled products. The objective of this study was to investigate the feasibility of use IR he...

  18. Thermal stability of liquid antioxidative extracts from pomegranate peel

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This research was carried out to assess the potential of using the natural antioxidants in pomegranate peel extracts as replacement for synthetic antioxidants. As a result the thermal stability of pomegranate peel extract products during sterilization and storage, and its effect on industrial, color...

  19. Optical wavelet transform for fingerprint identification

    NASA Astrophysics Data System (ADS)

    MacDonald, Robert P.; Rogers, Steven K.; Burns, Thomas J.; Fielding, Kenneth H.; Warhola, Gregory T.; Ruck, Dennis W.

    1994-03-01

    The Federal Bureau of Investigation (FBI) has recently sanctioned a wavelet fingerprint image compression algorithm developed for reducing storage requirements of digitized fingerprints. This research implements an optical wavelet transform of a fingerprint image, as the first step in an optical fingerprint identification process. Wavelet filters are created from computer- generated holograms of biorthogonal wavelets, the same wavelets implemented in the FBI algorithm. Using a detour phase holographic technique, a complex binary filter mask is created with both symmetry and linear phase. The wavelet transform is implemented with continuous shift using an optical correlation between binarized fingerprints written on a Magneto-Optic Spatial Light Modulator and the biorthogonal wavelet filters. A telescopic lens combination scales the transformed fingerprint onto the filters, providing a means of adjusting the biorthogonal wavelet filter dilation continuously. The wavelet transformed fingerprint is then applied to an optical fingerprint identification process. Comparison between normal fingerprints and wavelet transformed fingerprints shows improvement in the optical identification process, in terms of rotational invariance.

  20. Non-local mean denoising in diffusion tensor space

    PubMed Central

    SU, BAIHAI; LIU, QIANG; CHEN, JIE; WU, XI

    2014-01-01

    The aim of the present study was to present a novel non-local mean (NLM) method to denoise diffusion tensor imaging (DTI) data in the tensor space. Compared with the original NLM method, which uses intensity similarity to weigh the voxel, the proposed method weighs the voxel using tensor similarity measures in the diffusion tensor space. Euclidean distance with rotational invariance, and Riemannian distance and Log-Euclidean distance with affine invariance were implemented to compare the geometric and orientation features of the diffusion tensor comprehensively. The accuracy and efficacy of the proposed novel NLM method using these three similarity measures in DTI space, along with unbiased novel NLM in diffusion-weighted image space, were compared quantitatively and qualitatively in the present study. PMID:25009599

  1. Denoised Wigner distribution deconvolution via low-rank matrix completion.

    PubMed

    Lee, Justin; Barbastathis, George

    2016-09-01

    Wigner distribution deconvolution (WDD) is a decades-old method for recovering phase from intensity measurements. Although the technique offers an elegant linear solution to the quadratic phase retrieval problem, it has seen limited adoption due to its high computational/memory requirements and the fact that the technique often exhibits high noise sensitivity. Here, we propose a method for noise suppression in WDD via low-rank noisy matrix completion. Our technique exploits the redundancy of an object's phase space to denoise its WDD reconstruction. We show in model calculations that our technique outperforms other WDD algorithms as well as modern iterative methods for phase retrieval such as ptychography. Our results suggest that a class of phase retrieval techniques relying on regularized direct inversion of ptychographic datasets (instead of iterative reconstruction techniques) can provide accurate quantitative phase information in the presence of high levels of noise. PMID:27607616

  2. Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

    PubMed

    Pnevmatikakis, Eftychios A; Soudry, Daniel; Gao, Yuanjun; Machado, Timothy A; Merel, Josh; Pfau, David; Reardon, Thomas; Mu, Yu; Lacefield, Clay; Yang, Weijian; Ahrens, Misha; Bruno, Randy; Jessell, Thomas M; Peterka, Darcy S; Yuste, Rafael; Paninski, Liam

    2016-01-20

    We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data. PMID:26774160

  3. MRI noise estimation and denoising using non-local PCA.

    PubMed

    Manjón, José V; Coupé, Pierrick; Buades, Antonio

    2015-05-01

    This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases. PMID:25725303

  4. Locally adaptive bilateral clustering for universal image denoising

    NASA Astrophysics Data System (ADS)

    Toh, K. K. V.; Mat Isa, N. A.

    2012-12-01

    This paper presents a novel and efficient locally adaptive denoising method based on clustering of pixels into regions of similar geometric and radiometric structures. Clustering is performed by adaptively segmenting pixels in the local kernel based on their augmented variational series. Then, noise pixels are restored by selectively considering the radiometric and spatial properties of every pixel in the formed clusters. The proposed method is exceedingly robust in conveying reliable local structural information even in the presence of noise. As a result, the proposed method substantially outperforms other state-of-the-art methods in terms of image restoration and computational cost. We support our claims with ample simulated and real data experiments. The relatively fast runtime from extensive simulations also suggests that the proposed method is suitable for a variety of image-based products — either embedded in image capturing devices or applied as image enhancement software.

  5. Manual for Program PSTRESS: Peel stress computation

    NASA Technical Reports Server (NTRS)

    Barkey, Derek A.; Madan, Ram C.

    1987-01-01

    Described is the use of the interactive FORTRAN computer program PSTRESS, which computes a closed form solution for two bonded plates subjected to applied moments, vertical shears, and in-plane forces. The program calculates in-plane stresses in the plates, deflections of the plates, and peel and shear stresses in the adhesive. The document briefly outlines the analytical method used by PSTRESS, describes the input and output of the program, and presents a sample analysis. The results of the latter are shown to be within a few percent of results obtained using a NASTRAN finite element analysis. An appendix containing a listing of PSTRESS is included.

  6. Antibacterial activity of Citrus reticulata peel extracts.

    PubMed

    Jayaprakasha, G K; Negi, P S; Sikder, S; Rao, L J; Sakariah, K K

    2000-01-01

    Citrus peels were successively extracted with hexane, chloroform and acetone using a soxhlet extractor. The hexane and chloroform extracts were fractionated into alcohol-soluble and alcohol-insoluble fractions. These fractions were tested against different gram positive and gram negative bacteria. The EtOH-soluble fraction was found to be most effective. Fractionation of EtOH-soluble fraction on silica gel column yielded three polymethoxylated flavones, namely desmethylnobiletin, nobiletin and tangeretin. Their structures were confirmed by UV, 1H, 13C NMR and mass spectral studies. The findings indicated a potential of these natural compounds as biopreservatives in food applications. PMID:11204182

  7. Wavelet analysis in two-dimensional tomography

    NASA Astrophysics Data System (ADS)

    Burkovets, Dimitry N.

    2002-02-01

    The diagnostic possibilities of wavelet-analysis of coherent images of connective tissue in its pathological changes diagnostics. The effectiveness of polarization selection in obtaining wavelet-coefficients' images is also shown. The wavelet structures, characterizing the process of skin psoriasis, bone-tissue osteoporosis have been analyzed. The histological sections of physiological normal and pathologically changed samples of connective tissue of human skin and spongy bone tissue have been analyzed.

  8. Wavelet analysis of epileptic spikes

    NASA Astrophysics Data System (ADS)

    Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-05-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  9. Haar Wavelet Analysis of Climatic Time Series

    NASA Astrophysics Data System (ADS)

    Zhang, Zhihua; Moore, John; Grinsted, Aslak

    2014-05-01

    In order to extract the intrinsic information of climatic time series from background red noise, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. The relation between scale aand Fourier period T for the Morlet wavelet is a= 0.97T . However, for Haar wavelet, the corresponding formula is a= 0.37T . Since for any time series of time step δt and total length Nδt, the range of scales is from the smallest resolvable scale 2δt to the largest scale Nδt in wavelet-based time series analysis, by using the Haar wavelet analysis, one can extract more low frequency intrinsic information. Finally, we use our method to analyze Arctic Oscillation which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO,-commonly called a regime shift or tripping point. Our partial results have been published as follows: [1] Z. Zhang, J.C. Moore and A. Grinsted, Haar wavelet analysis of climatic time series, Int. J. Wavelets, Multiresol. & Inf. Process., in press, 2013 [2] Z. Zhang, J.C. Moore, Comment on "Significance tests for the wavelet power and the wavelet power spectrum", Ann. Geophys., 30:12, 2012

  10. Entangled Husimi Distribution and Complex Wavelet Transformation

    NASA Astrophysics Data System (ADS)

    Hu, Li-Yun; Fan, Hong-Yi

    2010-05-01

    Similar in spirit to the preceding work (Int. J. Theor. Phys. 48:1539, 2009) where the relationship between wavelet transformation and Husimi distribution function is revealed, we study this kind of relationship to the entangled case. We find that the optical complex wavelet transformation can be used to study the entangled Husimi distribution function in phase space theory of quantum optics. We prove that, up to a Gaussian function, the entangled Husimi distribution function of a two-mode quantum state | ψ> is just the modulus square of the complex wavelet transform of e^{-\\vert η \\vert 2/2} with ψ( η) being the mother wavelet.

  11. Wavelet Sparse Approximate Inverse Preconditioners

    NASA Technical Reports Server (NTRS)

    Chan, Tony F.; Tang, W.-P.; Wan, W. L.

    1996-01-01

    There is an increasing interest in using sparse approximate inverses as preconditioners for Krylov subspace iterative methods. Recent studies of Grote and Huckle and Chow and Saad also show that sparse approximate inverse preconditioner can be effective for a variety of matrices, e.g. Harwell-Boeing collections. Nonetheless a drawback is that it requires rapid decay of the inverse entries so that sparse approximate inverse is possible. However, for the class of matrices that, come from elliptic PDE problems, this assumption may not necessarily hold. Our main idea is to look for a basis, other than the standard one, such that a sparse representation of the inverse is feasible. A crucial observation is that the kind of matrices we are interested in typically have a piecewise smooth inverse. We exploit this fact, by applying wavelet techniques to construct a better sparse approximate inverse in the wavelet basis. We shall justify theoretically and numerically that our approach is effective for matrices with smooth inverse. We emphasize that in this paper we have only presented the idea of wavelet approximate inverses and demonstrated its potential but have not yet developed a highly refined and efficient algorithm.

  12. Visibility of Wavelet Quantization Noise

    NASA Technical Reports Server (NTRS)

    Watson, Andrew B.; Yang, Gloria Y.; Solomon, Joshua A.; Villasenor, John; Null, Cynthia H. (Technical Monitor)

    1995-01-01

    The Discrete Wavelet Transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter, which we call DWT uniform quantization noise. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2(exp)-L , where r is display visual resolution in pixels/degree, and L is the wavelet level. Amplitude thresholds increase rapidly with spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from low-pass to horizontal/vertical to diagonal. We describe a mathematical model to predict DWT noise detection thresholds as a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

  13. Peeling of elastic thin films on heterogeneous solids

    NASA Astrophysics Data System (ADS)

    Ponson, Laurent; Xia, Shuman; Ravichandran, Guruswamy; Bhattacharya, Kaushik

    2010-03-01

    Thin film adhesives have become increasingly important in various applications such as packaging and coating, and we benefit daily of their adhesion properties by using various kinds of tape. Here, we study the effect of heterogeneities on their peeling properties. From the theoretical side, we show how perturbations of the peeling front induced by heterogeneities of fracture energy at the film-substrate interface result in additional bending and stretching of the thin film. The energetical cost associated with these deformations is balanced by the wandering of the peeling front that takes advantage of area of lower interfacial fracture energy. This leads to various peeling front geometries that are calculated as a function of the landscape of fracture energy. These predictions are confronted with experimental measurements performed on a model system where we follow in real time the adhesion front during the peeling of an elastic thin film on a rigid substrate with controlled heterogeneous properties. A PDMS thin film produced by spin coating is peeled from a rigid solid on which patterns are designed by using a regular printer, taking advantage of the high adhesion energy of the ink-PDMS interface. The measured peeling front geometry is compared with the theoretical predictions and the toughening effect induced by the heterogeneities is finally discussed.

  14. Ripening influences banana and plantain peels composition and energy content.

    PubMed

    Emaga, Thomas Happi; Bindelle, Jérôme; Agneesens, Richard; Buldgen, André; Wathelet, Bernard; Paquot, Michel

    2011-01-01

    Musa sp. peels are widely used by smallholders as complementary feeds for cattle in the tropics. A study of the influence of the variety and the maturation stage of the fruit on fermentability and metabolisable energy (ME) content of the peels was performed using banana (Yangambi Km5) and plantain (Big Ebanga) peels at three stages of maturation in an in vitro model of the rumen. Peel samples were analysed for starch, free sugars and fibre composition. Samples were incubated in the presence of rumen fluid. Kinetics of gas production were modelled, ME content was calculated using prediction equation and short-chain fatty acids production and molar ratio were measured after 72 h of fermentation. Final gas production was higher in plantain (269-339 ml g(-1)) compared to banana (237-328 ml g(-1)) and plantain exhibited higher ME contents (8.9-9.7 MJ/kg of dry matter, DM) compared to banana (7.7-8.8 MJ/kg of DM). Butyrate molar ratio decreased with maturity of the peels. The main influence of the variety and the stage of maturation on all fermentation parameters as well as ME contents of the peels was correlated to changes in the carbohydrate fraction of the peels, including starch and fibre. PMID:20725857

  15. Ripening influences banana and plantain peels composition and energy content.

    PubMed

    Emaga, Thomas Happi; Bindelle, Jérôme; Agneesens, Richard; Buldgen, André; Wathelet, Bernard; Paquot, Michel

    2011-01-01

    Musa sp. peels are widely used by smallholders as complementary feeds for cattle in the tropics. A study of the influence of the variety and the maturation stage of the fruit on fermentability and metabolisable energy (ME) content of the peels was performed using banana (Yangambi Km5) and plantain (Big Ebanga) peels at three stages of maturation in an in vitro model of the rumen. Peel samples were analysed for starch, free sugars and fibre composition. Samples were incubated in the presence of rumen fluid. Kinetics of gas production were modelled, ME content was calculated using prediction equation and short-chain fatty acids production and molar ratio were measured after 72 h of fermentation. Final gas production was higher in plantain (269-339 ml g(-1)) compared to banana (237-328 ml g(-1)) and plantain exhibited higher ME contents (8.9-9.7 MJ/kg of dry matter, DM) compared to banana (7.7-8.8 MJ/kg of DM). Butyrate molar ratio decreased with maturity of the peels. The main influence of the variety and the stage of maturation on all fermentation parameters as well as ME contents of the peels was correlated to changes in the carbohydrate fraction of the peels, including starch and fibre.

  16. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

    PubMed Central

    Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P. Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

    2014-01-01

    The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www

  17. Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet

    PubMed Central

    Nguyen, Nha; Huang, Heng; Oraintara, Soontorn; Vo, An

    2010-01-01

    Motivation: Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise. Results: We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results. Availability: http://ranger.uta.edu/∼heng/MS/GDWavelet.html or http://www.naaan.org/nhanguyen/archive.htm Contact: heng

  18. A new wavelet-based thin plate element using B-spline wavelet on the interval

    NASA Astrophysics Data System (ADS)

    Jiawei, Xiang; Xuefeng, Chen; Zhengjia, He; Yinghong, Zhang

    2008-01-01

    By interacting and synchronizing wavelet theory in mathematics and variational principle in finite element method, a class of wavelet-based plate element is constructed. In the construction of wavelet-based plate element, the element displacement field represented by the coefficients of wavelet expansions in wavelet space is transformed into the physical degree of freedoms in finite element space via the corresponding two-dimensional C1 type transformation matrix. Then, based on the associated generalized function of potential energy of thin plate bending and vibration problems, the scaling functions of B-spline wavelet on the interval (BSWI) at different scale are employed directly to form the multi-scale finite element approximation basis so as to construct BSWI plate element via variational principle. BSWI plate element combines the accuracy of B-spline functions approximation and various wavelet-based elements for structural analysis. Some static and dynamic numerical examples are studied to demonstrate the performances of the present element.

  19. Onion-peeling inversion of stellarator images

    NASA Astrophysics Data System (ADS)

    Hammond, K. C.; Diaz-Pacheco, R. R.; Kornbluth, Y.; Volpe, F. A.; Wei, Y.

    2016-11-01

    An onion-peeling technique is developed for inferring the emissivity profile of a stellarator plasma from a two-dimensional image acquired through a CCD or CMOS camera. Each pixel in the image is treated as an integral of emission along a particular line-of-sight. Additionally, the flux surfaces in the plasma are partitioned into discrete layers, each of which is assumed to have uniform emissivity. If the topology of the flux surfaces is known, this construction permits the development of a system of linear equations that can be solved for the emissivity of each layer. We present initial results of this method applied to wide-angle visible images of the Columbia Neutral Torus (CNT) stellarator plasma.

  20. Banana peel: an effective biosorbent for aflatoxins.

    PubMed

    Shar, Zahid Hussain; Fletcher, Mary T; Sumbal, Gul Amer; Sherazi, Syed Tufail Hussain; Giles, Cindy; Bhanger, Muhammad Iqbal; Nizamani, Shafi Muhammad

    2016-05-01

    This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pHpzc) analysis were used to characterise the adsorbent material. Aflatoxins' adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6-8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q0) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg(-1) for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets. PMID:27052947

  1. Banana peel: an effective biosorbent for aflatoxins.

    PubMed

    Shar, Zahid Hussain; Fletcher, Mary T; Sumbal, Gul Amer; Sherazi, Syed Tufail Hussain; Giles, Cindy; Bhanger, Muhammad Iqbal; Nizamani, Shafi Muhammad

    2016-05-01

    This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pHpzc) analysis were used to characterise the adsorbent material. Aflatoxins' adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6-8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q0) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg(-1) for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets.

  2. Edge-preserving image denoising via group coordinate descent on the GPU

    PubMed Central

    McGaffin, Madison G.; Fessler, Jeffrey A.

    2015-01-01

    Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel one-dimensional pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates inplace and only store the noisy data, denoised image and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation (TV). Both algorithms use the majorize-minimize (MM) framework to solve the one-dimensional pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time. PMID:25675454

  3. Total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising

    NASA Astrophysics Data System (ADS)

    Wu, Zhaojun; Wang, Qiang; Wu, Zhenghua; Shen, Yi

    2016-01-01

    Many nuclear norm minimization (NNM)-based methods have been proposed for hyperspectral image (HSI) mixed denoising due to the low-rank (LR) characteristics of clean HSI. However, the NNM-based methods regularize each eigenvalue equally, which is unsuitable for the denoising problem, where each eigenvalue stands for special physical meaning and should be regularized differently. However, the NNM-based methods only exploit the high spectral correlation, while ignoring the local structure of HSI and resulting in spatial distortions. To address these problems, a total variation (TV)-regularized weighted nuclear norm minimization (TWNNM) method is proposed. To obtain the desired denoising performance, two issues are included. First, to exploit the high spectral correlation, the HSI is restricted to be LR, and different eigenvalues are minimized with different weights based on the WNNM. Second, to preserve the local structure of HSI, the TV regularization is incorporated, and the alternating direction method of multipliers is used to solve the resulting optimization problem. Both simulated and real data experiments demonstrate that the proposed TWNNM approach produces superior denoising results for the mixed noise case in comparison with several state-of-the-art denoising methods.

  4. Edge-preserving image denoising via group coordinate descent on the GPU.

    PubMed

    McGaffin, Madison Gray; Fessler, Jeffrey A

    2015-04-01

    Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.

  5. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights.

    PubMed

    Deledalle, Charles-Alban; Denis, Loïc; Tupin, Florence

    2009-12-01

    Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades , which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

  6. [An Improved Empirical Mode Decomposition Algorithm for Phonocardiogram Signal De-noising and Its Application in S1/S2 Extraction].

    PubMed

    Gong, Jing; Nie, Shengdong; Wang, Yuanjun

    2015-10-01

    In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.

  7. A fast-convergence POCS seismic denoising and reconstruction method

    NASA Astrophysics Data System (ADS)

    Ge, Zi-Jian; Li, Jing-Ye; Pan, Shu-Lin; Chen, Xiao-Hong

    2015-06-01

    The efficiency, precision, and denoising capabilities of reconstruction algorithms are critical to seismic data processing. Based on the Fourier-domain projection onto convex sets (POCS) algorithm, we propose an inversely proportional threshold model that defines the optimum threshold, in which the descent rate is larger than in the exponential threshold in the large-coefficient section and slower than in the exponential threshold in the small-coefficient section. Thus, the computation efficiency of the POCS seismic reconstruction greatly improves without affecting the reconstructed precision of weak reflections. To improve the flexibility of the inversely proportional threshold, we obtain the optimal threshold by using an adjustable dependent variable in the denominator of the inversely proportional threshold model. For random noise attenuation by completing the missing traces in seismic data reconstruction, we present a weighted reinsertion strategy based on the data-driven model that can be obtained by using the percentage of the data-driven threshold in each iteration in the threshold section. We apply the proposed POCS reconstruction method to 3D synthetic and field data. The results suggest that the inversely proportional threshold model improves the computational efficiency and precision compared with the traditional threshold models; furthermore, the proposed reinserting weight strategy increases the SNR of the reconstructed data.

  8. Stacked Denoising Autoencoders Applied to Star/Galaxy Classification

    NASA Astrophysics Data System (ADS)

    Qin, H. R.; Lin, J. M.; Wang, J. Y.

    2016-05-01

    In recent years, the deep learning has been becoming more and more popular because it is well-adapted, and has a high accuracy and complex structure, but it has not been used in astronomy. In order to resolve the question that the classification accuracy of star/galaxy is high on the bright set, but low on the faint set of the Sloan Digital Sky Survey (SDSS), we introduce the new deep learning SDA (stacked denoising autoencoders) and dropout technology, which can greatly improve robustness and anti-noise performance. We randomly selected the bright source set and faint source set from DR12 and DR7 with spectroscopic measurements, and preprocessed them. Afterwards, we randomly selected the training set and testing set without replacement from the bright set and faint set. At last, we used the obtained training set to train the SDA model of SDSS-DR7 and SDSS-DR12. We compared the testing result with the results of Library for Support Vector Machines (LibSVM), J48, Logistic Model Trees (LMT), Support Vector Machine (SVM), Logistic Regression, and Decision Stump algorithm on the SDSS-DR12 testing set, and the results of six kinds of decision trees on the SDSS-DR7 testing set. The simulation shows that SDA has a better classification accuracy than other machine learning algorithms. When we use completeness function as the test parameter, the test accuracy rate is improved by about 15% on the faint set of SDSS-DR7.

  9. Unsupervised dealiasing and denoising of color-Doppler data.

    PubMed

    Muth, Stéphan; Dort, Sarah; Sebag, Igal A; Blais, Marie-Josée; Garcia, Damien

    2011-08-01

    Color Doppler imaging (CDI) is the premiere modality to analyze blood flow in clinical practice. In the prospect of producing new CDI-based tools, we developed a fast unsupervised denoiser and dealiaser (DeAN) algorithm for color Doppler raw data. The proposed technique uses robust and automated image post-processing techniques that make the DeAN clinically compliant. The DeAN includes three consecutive advanced and hands-off numerical tools: (1) statistical region merging segmentation, (2) recursive dealiasing process, and (3) regularized robust smoothing. The performance of the DeAN was evaluated using Monte-Carlo simulations on mock Doppler data corrupted by aliasing and inhomogeneous noise. Fifty aliased Doppler images of the left ventricle acquired with a clinical ultrasound scanner were also analyzed. The analytical study demonstrated that color Doppler data can be reconstructed with high accuracy despite the presence of strong corruption. The normalized RMS error on the numerical data was less than 8% even with signal-to-noise ratio as low as 10dB. The algorithm also allowed us to recover highly reliable Doppler flows in clinical data. The DeAN is fast, accurate and not observer-dependent. Preliminary results showed that it is also directly applicable to 3-D data. This will offer the possibility of developing new tools to better decipher the blood flow dynamics in cardiovascular diseases.

  10. Load identification approach based on basis pursuit denoising algorithm

    NASA Astrophysics Data System (ADS)

    Ginsberg, D.; Ruby, M.; Fritzen, C. P.

    2015-07-01

    The information of the external loads is of great interest in many fields of structural analysis, such as structural health monitoring (SHM) systems or assessment of damage after extreme events. However, in most cases it is not possible to measure the external forces directly, so they need to be reconstructed. Load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response functions are usually the knowns. Generally, this leads to a so called ill-posed inverse problem, which involves solving an underdetermined linear system of equations. For most practical applications it can be assumed that the applied loads are not arbitrarily distributed in time and space, at least some specific characteristics about the external excitation are known a priori. In this contribution this knowledge was used to develop a more suitable force reconstruction method, which allows identifying the time history and the force location simultaneously by employing significantly fewer sensors compared to other reconstruction approaches. The properties of the external force are used to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The possibility of reconstructing loads based on noisy structural measurement signals will be demonstrated by considering two frequently occurring loading conditions: harmonic excitation and impact events, separately and combined. First a simulation study of a simple plate structure is carried out and thereafter an experimental investigation of a real beam is performed.

  11. Generalized non-local means filtering for image denoising

    NASA Astrophysics Data System (ADS)

    Dolui, Sudipto; Salgado Patarroyo, Iván. C.; Michailovich, Oleg V.

    2014-02-01

    Non-local means (NLM) filtering has been shown to outperform alternative denoising methodologies under the model of additive white Gaussian noise contamination. Recently, several theoretical frameworks have been developed to extend this class of algorithms to more general types of noise statistics. However, many of these frameworks are specifically designed for a single noise contamination model, and are far from optimal across varying noise statistics. The NLM filtering techniques rely on the definition of a similarity measure, which quantifies the similarity of two neighbourhoods along with their respective centroids. The key to the unification of the NLM filter for different noise statistics lies in the definition of a universal similarity measure which is guaranteed to provide favourable performance irrespective of the statistics of the noise. Accordingly, the main contribution of this work is to provide a rigorous statistical framework to derive such a universal similarity measure, while highlighting some of its theoretical and practical favourable characteristics. Additionally, the closed form expressions of the proposed similarity measure are provided for a number of important noise scenarios and the practical utility of the proposed similarity measure is demonstrated through numerical experiments.

  12. Fast fractal image compression with triangulation wavelets

    NASA Astrophysics Data System (ADS)

    Hebert, D. J.; Soundararajan, Ezekiel

    1998-10-01

    We address the problem of improving the performance of wavelet based fractal image compression by applying efficient triangulation methods. We construct iterative function systems (IFS) in the tradition of Barnsley and Jacquin, using non-uniform triangular range and domain blocks instead of uniform rectangular ones. We search for matching domain blocks in the manner of Zhang and Chen, performing a fast wavelet transform on the blocks and eliminating low resolution mismatches to gain speed. We obtain further improvements by the efficiencies of binary triangulations (including the elimination of affine and symmetry calculations and reduced parameter storage), and by pruning the binary tree before construction of the IFS. Our wavelets are triangular Haar wavelets and `second generation' interpolation wavelets as suggested by Sweldens' recent work.

  13. Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry

    PubMed Central

    Chiron, Lionel; van Agthoven, Maria A.; Kieffer, Bruno; Rolando, Christian; Delsuc, Marc-André

    2014-01-01

    Modern scientific research produces datasets of increasing size and complexity that require dedicated numerical methods to be processed. In many cases, the analysis of spectroscopic data involves the denoising of raw data before any further processing. Current efficient denoising algorithms require the singular value decomposition of a matrix with a size that scales up as the square of the data length, preventing their use on very large datasets. Taking advantage of recent progress on random projection and probabilistic algorithms, we developed a simple and efficient method for the denoising of very large datasets. Based on the QR decomposition of a matrix randomly sampled from the data, this approach allows a gain of nearly three orders of magnitude in processing time compared with classical singular value decomposition denoising. This procedure, called urQRd (uncoiled random QR denoising), strongly reduces the computer memory footprint and allows the denoising algorithm to be applied to virtually unlimited data size. The efficiency of these numerical tools is demonstrated on experimental data from high-resolution broadband Fourier transform ion cyclotron resonance mass spectrometry, which has applications in proteomics and metabolomics. We show that robust denoising is achieved in 2D spectra whose interpretation is severely impaired by scintillation noise. These denoising procedures can be adapted to many other data analysis domains where the size and/or the processing time are crucial. PMID:24390542

  14. Understanding Surface Adhesion in Nature: A Peeling Model

    PubMed Central

    Gu, Zhen; Li, Siheng; Zhang, Feilong

    2016-01-01

    Nature often exhibits various interesting and unique adhesive surfaces. The attempt to understand the natural adhesion phenomena can continuously guide the design of artificial adhesive surfaces by proposing simplified models of surface adhesion. Among those models, a peeling model can often effectively reflect the adhesive property between two surfaces during their attachment and detachment processes. In the context, this review summarizes the recent advances about the peeling model in understanding unique adhesive properties on natural and artificial surfaces. It mainly includes four parts: a brief introduction to natural surface adhesion, the theoretical basis and progress of the peeling model, application of the peeling model, and finally, conclusions. It is believed that this review is helpful to various fields, such as surface engineering, biomedicine, microelectronics, and so on. PMID:27812476

  15. Salicylic acid as a peeling agent: a comprehensive review

    PubMed Central

    Arif, Tasleem

    2015-01-01

    Salicylic acid has been used to treat various skin disorders for more than 2,000 years. The ability of salicylic acid to exfoliate the stratum corneum makes it a good agent for peeling. In particular, the comedolytic property of salicylic acid makes it a useful peeling agent for patients with acne. Once considered as a keratolytic agent, the role of salicylic acid as a desmolytic agent, because of its ability to disrupt cellular junctions rather than breaking or lysing intercellular keratin filaments, is now recognized and is discussed here. Salicylic acid as a peeling agent has a number of indications, including acne vulgaris, melasma, photodamage, freckles, and lentigines. The efficacy and safety of salicylic acid peeling in Fitzpatrick skin types I–III as well as in skin types V and VI have been well documented in the literature. This paper reviews the available data and literature on salicylic acid as a peeling agent and its possible indications. Its properties, efficacy and safety, the peeling procedure, and possible side effects are discussed in detail. An account of salicylism is also included. PMID:26347269

  16. Salicylic acid as a peeling agent: a comprehensive review.

    PubMed

    Arif, Tasleem

    2015-01-01

    Salicylic acid has been used to treat various skin disorders for more than 2,000 years. The ability of salicylic acid to exfoliate the stratum corneum makes it a good agent for peeling. In particular, the comedolytic property of salicylic acid makes it a useful peeling agent for patients with acne. Once considered as a keratolytic agent, the role of salicylic acid as a desmolytic agent, because of its ability to disrupt cellular junctions rather than breaking or lysing intercellular keratin filaments, is now recognized and is discussed here. Salicylic acid as a peeling agent has a number of indications, including acne vulgaris, melasma, photodamage, freckles, and lentigines. The efficacy and safety of salicylic acid peeling in Fitzpatrick skin types I-III as well as in skin types V and VI have been well documented in the literature. This paper reviews the available data and literature on salicylic acid as a peeling agent and its possible indications. Its properties, efficacy and safety, the peeling procedure, and possible side effects are discussed in detail. An account of salicylism is also included.

  17. Characterization of peeling modes in a low aspect ratio tokamak

    NASA Astrophysics Data System (ADS)

    Bongard, M. W.; Thome, K. E.; Barr, J. L.; Burke, M. G.; Fonck, R. J.; Hinson, E. T.; Redd, A. J.; Schlossberg, D. J.

    2014-11-01

    Peeling modes are observed at the plasma edge in the Pegasus Toroidal Experiment under conditions of high edge current density (Jedge ˜ 0.1 MA m-2) and low magnetic field (B ˜ 0.1 T) present at near-unity aspect ratio. Their macroscopic properties are measured using external Mirnov coil arrays, Langmuir probes and high-speed visible imaging. The modest edge parameters and short pulse lengths of Pegasus discharges permit direct measurement of the internal magnetic field structure with an insertable array of Hall-effect sensors, providing the current profile and its temporal evolution. Peeling modes generate coherent, edge-localized electromagnetic activity with low toroidal mode numbers n ⩽ 3 and high poloidal mode numbers, in agreement with theoretical expectations of a low-n external kink structure. Coherent MHD fluctuation amplitudes are found to be strongly dependent on the experimentally measured Jedge/B peeling instability drive, consistent with theory. Peeling modes nonlinearly generate ELM-like, field-aligned filamentary structures that detach from the edge and propagate radially outward. The KFIT equilibrium code is extended with an Akima spline profile parameterization and an improved model for induced toroidal wall current estimation to obtain a reconstruction during peeling activity with its current profile constrained by internal Hall measurements. It is used to test the analytic peeling stability criterion and numerically evaluate ideal MHD stability. Both approaches predict instability, in agreement with experiment, with the latter identifying an unstable external kink.

  18. Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm

    PubMed Central

    Fadaee, Mojtaba; Shamsi, Mousa; Saberkari, Hamidreza; Sedaaghi, Mohammad Hossein

    2015-01-01

    In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L2 norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods. PMID:26955565

  19. Improved DCT-based nonlocal means filter for MR images denoising.

    PubMed

    Hu, Jinrong; Pu, Yifei; Wu, Xi; Zhang, Yi; Zhou, Jiliu

    2012-01-01

    The nonlocal means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter based on the discrete cosine transform (DCT). Instead of computing similarity weights using the gray level information directly, the proposed method calculates similarity weights in the DCT subspace of neighborhood. Due to promising characteristics of DCT, such as low data correlation and high energy compaction, the proposed filter is naturally endowed with more accurate estimation of weights thus enhances denoising effectively. The performance of the proposed filter is evaluated qualitatively and quantitatively together with two other NLM filters, namely, the original NLM filter and the unbiased NLM (UNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance in MRI compared to the others.

  20. Improved extreme value weighted sparse representational image denoising with random perturbation

    NASA Astrophysics Data System (ADS)

    Xuan, Shibin; Han, Yulan

    2015-11-01

    Research into the removal of mixed noise is a hot topic in the field of image denoising. Currently, weighted encoding with sparse nonlocal regularization represents an excellent mixed noise removal method. To make the fitting function closer to the requirements of a robust estimation technique, an extreme value technique is used that allows the fitting function to satisfy three conditions of robust estimation on a larger interval. Moreover, a random disturbance sequence is integrated into the denoising model to prevent the iterative solving process from falling into local optima. A radon transform-based noise detection algorithm and an adaptive median filter are used to obtain a high-quality initial solution for the iterative procedure of the image denoising model. Experimental results indicate that this improved method efficiently enhances the weighted encoding with a sparse nonlocal regularization model. The proposed method can effectively remove mixed noise from corrupted images, while better preserving the edges and details of the processed image.

  1. Evidence and considerations in the application of chemical peels in skin disorders and aesthetic resurfacing.

    PubMed

    Rendon, Marta I; Berson, Diane S; Cohen, Joel L; Roberts, Wendy E; Starker, Isaac; Wang, Beatrice

    2010-07-01

    Chemical peeling is a popular, relatively inexpensive, and generally safe method for treatment of some skin disorders and to refresh and rejuvenate skin. This article focuses on chemical peels and their use in routine clinical practice. Chemical peels are classified by the depth of action into superficial, medium, and deep peels. The depth of the peel is correlated with clinical changes, with the greatest change achieved by deep peels. However, the depth is also associated with longer healing times and the potential for complications. A wide variety of peels are available, utilizing various topical agents and concentrations, including a recent salicylic acid derivative, beta-lipohydroxy acid, which has properties that may expand the clinical use of peels. Superficial peels, penetrating only the epidermis, can be used to enhance treatment for a variety of conditions, including acne, melasma, dyschromias, photodamage, and actinic keratoses. Medium-depth peels, penetrating to the papillary dermis, may be used for dyschromia, multiple solar keratoses, superficial scars, and pigmentary disorders. Deep peels, affecting reticular dermis, may be used for severe photoaging, deep wrinkles, or scars. Peels can be combined with other in-office facial resurfacing techniques to optimize outcomes and enhance patient satisfaction and allow clinicians to tailor the treatment to individual patient needs. Successful outcomes are based on a careful patient selection as well as appropriate use of specific peeling agents. Used properly, the chemical peel has the potential to fill an important therapeutic need in the dermatologist's and plastic surgeon's armamentarium. PMID:20725555

  2. Evidence and Considerations in the Application of Chemical Peels in Skin Disorders and Aesthetic Resurfacing

    PubMed Central

    Berson, Diane S.; Cohen, Joel L.; Roberts, Wendy E.; Starker, Isaac; Wang, Beatrice

    2010-01-01

    Chemical peeling is a popular, relatively inexpensive, and generally safe method for treatment of some skin disorders and to refresh and rejuvenate skin. This article focuses on chemical peels and their use in routine clinical practice. Chemical peels are classified by the depth of action into superficial, medium, and deep peels. The depth of the peel is correlated with clinical changes, with the greatest change achieved by deep peels. However, the depth is also associated with longer healing times and the potential for complications. A wide variety of peels are available, utilizing various topical agents and concentrations, including a recent salicylic acid derivative, β-lipohydroxy acid, which has properties that may expand the clinical use of peels. Superficial peels, penetrating only the epidermis, can be used to enhance treatment for a variety of conditions, including acne, melasma, dyschromias, photodamage, and actinic keratoses. Medium-depth peels, penetrating to the papillary dermis, may be used for dyschromia, multiple solar keratoses, superficial scars, and pigmentary disorders. Deep peels, affecting reticular dermis, may be used for severe photoaging, deep wrinkles, or scars. Peels can be combined with other in-office facial resurfacing techniques to optimize outcomes and enhance patient satisfaction and allow clinicians to tailor the treatment to individual patient needs. Successful outcomes are based on a careful patient selection as well as appropriate use of specific peeling agents. Used properly, the chemical peel has the potential to fill an important therapeutic need in the dermatologist's and plastic surgeon's armamentarium. PMID:20725555

  3. Deformation of nanotubes in peeling contact with flat substrate: An in situ electron microscopy nanomechanical study

    NASA Astrophysics Data System (ADS)

    Chen, Xiaoming; Zheng, Meng; Wei, Qing; Signetti, Stefano; Pugno, Nicola M.; Ke, Changhong

    2016-04-01

    Peeling of one-dimensional (1D) nanostructures from flat substrates is an essential technique in studying their adhesion properties. The mechanical deformation of the nanostructure in the peeling experiment is critical to the understanding of the peeling process and the interpretation of the peeling measurements, but it is challenging to measure directly and quantitatively at the nanoscale. Here, we investigate the peeling deformation of a bundled carbon nanotube (CNT) fiber by using an in situ scanning electron microscopy nanomechanical peeling technique. A pre-calibrated atomic force microscopy cantilever is utilized as the peeling force sensor, and its back surface acts as the peeling contact substrate. The nanomechanical peeling scheme enables a quantitative characterization of the deformational behaviors of the CNT fiber in both positive and negative peeling configurations with sub-10 nm spatial and sub-nN force resolutions. Nonlinear continuum mechanics models and finite element simulations are employed to interpret the peeling measurements. The measurements and analysis reveal that the structural imperfections in the CNT fiber may have a substantial influence on its peeling deformations and the corresponding peeling forces. The research findings reported in this work are useful to the study of mechanical and adhesion properties of 1D nanostructures by using nanomechanical peeling techniques.

  4. Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

    NASA Astrophysics Data System (ADS)

    Skiadopoulos, S.; Karatrantou, A.; Korfiatis, P.; Costaridou, L.; Vassilakos, P.; Apostolopoulos, D.; Panayiotakis, G.

    2009-10-01

    The statistical nature of single photon emission computed tomography (SPECT) imaging, due to the Poisson noise effect, results in the degradation of image quality, especially in the case of lesions of low signal-to-noise ratio (SNR). A variety of well-established single-scale denoising methods applied on projection raw images have been incorporated in SPECT imaging applications, while multi-scale denoising methods with promising performance have been proposed. In this paper, a comparative evaluation study is performed between a multi-scale platelet denoising method and the well-established Butterworth filter applied as a pre- and post-processing step on images reconstructed without and/or with attenuation correction. Quantitative evaluation was carried out employing (i) a cardiac phantom containing two different size cold defects, utilized in two experiments conducted to simulate conditions without and with photon attenuation from myocardial surrounding tissue and (ii) a pilot-verified clinical dataset of 15 patients with ischemic defects. Image noise, defect contrast, SNR and defect contrast-to-noise ratio (CNR) metrics were computed for both phantom and patient defects. In addition, an observer preference study was carried out for the clinical dataset, based on rankings from two nuclear medicine clinicians. Without photon attenuation conditions, denoising by platelet and Butterworth post-processing methods outperformed Butterworth pre-processing for large size defects, while for small size defects, as well as with photon attenuation conditions, all methods have demonstrated similar denoising performance. Under both attenuation conditions, the platelet method showed improved performance with respect to defect contrast, SNR and defect CNR in the case of images reconstructed without attenuation correction, however not statistically significant (p > 0.05). Quantitative as well as preference results obtained from clinical data showed similar performance of the

  5. MR images denoising using DCT-based unbiased nonlocal means filter

    NASA Astrophysics Data System (ADS)

    Zheng, Xiuqing; Hu, Jinrong; Zhou, Jiuliu

    2013-03-01

    The non-local means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter by using a low-pass filtered and low dimensional version of neighborhood for calculating the similarity weights. The discrete cosine transform (DCT) is used as a smoothing kernel, allowing both improvements in similarity estimation and computational speed-up. Experimental results show that the proposed filter achieves better denoising performance in MR Images compared to others filters, such as recently proposed NLM filter and unbiased NLM (UNLM) filter.

  6. Imaging system of wavelet optics described by the Gaussian linear frequency-modulated complex wavelet.

    PubMed

    Tan, Liying; Ma, Jing; Wang, Guangming

    2005-12-01

    The image formation and the point-spread function of an optical system are analyzed by use of the wavelet basis function. The image described by a wavelet is no longer an indivisible whole image. It is, rather, a complex image consisting of many wavelet subimages, which come from the changes of different parameters (scale) a and c, and parameters b and d show the positions of wavelet subimages under different scales. A Gaussian frequency-modulated complex-valued wavelet function is introduced to express the point-spread function of an optical system and used to describe the image formation. The analysis, in allusion to the situation of illumination with a monochromatic plain light wave, shows that using the theory of wavelet optics to describe the image formation of an optical system is feasible.

  7. Imaging system of wavelet optics described by the Gaussian linear frequency-modulated complex wavelet

    NASA Astrophysics Data System (ADS)

    Tan, Liying; Ma, Jing; Wang, Guangming

    2005-12-01

    The image formation and the point-spread function of an optical system are analyzed by use of the wavelet basis function. The image described by a wavelet is no longer an indivisible whole image. It is, rather, a complex image consisting of many wavelet subimages, which come from the changes of different parameters (scale) a and c, and parameters b and d show the positions of wavelet subimages under different scales. A Gaussian frequency-modulated complex-valued wavelet function is introduced to express the point-spread function of an optical system and used to describe the image formation. The analysis, in allusion to the situation of illumination with a monochromatic plain light wave, shows that using the theory of wavelet optics to describe the image formation of an optical system is feasible.

  8. Applications of a fast, continuous wavelet transform

    SciTech Connect

    Dress, W.B.

    1997-02-01

    A fast, continuous, wavelet transform, based on Shannon`s sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon`s sampling theorem lets us view the Fourier transform of the data set as a continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time- domain sampling of the signal under analysis. Computational cost and nonorthogonality aside, the inherent flexibility and shift invariance of the frequency-space wavelets has advantages. The method has been applied to forensic audio reconstruction speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants` heart beats. Audio reconstruction is aided by selection of desired regions in the 2-D representation of the magnitude of the transformed signal. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass-spring system (e.g., a vehicle) by an occupants beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, features such as the glottal closing rate and word and phrase segmentation may be extracted from voice data.

  9. [Hyper spectral estimation method for soil alkali hydrolysable nitrogen content based on discrete wavelet transform and genetic algorithm in combining with partial least squares DWT-GA-PLS)].

    PubMed

    Chen, Hong-Yan; Zhao, Geng-Xing; Li, Xi-Can; Wang, Xiang-Feng; Li, Yu-Ling

    2013-11-01

    Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content.

  10. Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

    PubMed Central

    Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

    2015-01-01

    In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532

  11. Wavelet-based multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Liu, Dian-Ting; Zhou, Xiao-Dan; Wang, Cheng-Wen

    2008-09-01

    This paper proposes a novel wavelet-based face recognition method using thermal infrared (IR) and visible-light face images. The method applies the combination of Gabor and the Fisherfaces method to the reconstructed IR and visible images derived from wavelet frequency subbands. Our objective is to search for the subbands that are insensitive to the variation in expression and in illumination. The classification performance is improved by combining the multispectal information coming from the subbands that attain individually low equal error rate. Experimental results on Notre Dame face database show that the proposed wavelet-based algorithm outperforms previous multispectral images fusion method as well as monospectral method.

  12. Wavelet Applications for Flight Flutter Testing

    NASA Technical Reports Server (NTRS)

    Lind, Rick; Brenner, Marty; Freudinger, Lawrence C.

    1999-01-01

    Wavelets present a method for signal processing that may be useful for analyzing responses of dynamical systems. This paper describes several wavelet-based tools that have been developed to improve the efficiency of flight flutter testing. One of the tools uses correlation filtering to identify properties of several modes throughout a flight test for envelope expansion. Another tool uses features in time-frequency representations of responses to characterize nonlinearities in the system dynamics. A third tool uses modulus and phase information from a wavelet transform to estimate modal parameters that can be used to update a linear model and reduce conservatism in robust stability margins.

  13. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

    PubMed Central

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2015-01-01

    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. PMID:26593918

  14. Long-term potentiation: peeling the onion.

    PubMed

    Nicoll, Roger A; Roche, Katherine W

    2013-11-01

    Since the discovery of long-term potentiation (LTP), thousands of papers have been published on this phenomenon. With this massive amount of information, it is often difficult, especially for someone not directly involved in the field, not to be overwhelmed. The goal of this review is to peel away as many layers as possible, and probe the core properties of LTP. We would argue that the many dozens of proteins that have been implicated in the phenomenon are not essential, but rather modulate, often in indirect ways, the threshold and/or magnitude of LTP. What is required is NMDA receptor activation followed by CaMKII activation. The consequence of CaMKII activation is the rapid recruitment of AMPA receptors to the synapse. This recruitment is independent of AMPA receptor subunit type, but absolutely requires an adequate pool of surface receptors. An important unresolved issue is how exactly CaMKII activation leads to modifications in the PSD to allow rapid enrichment. This article is part of the Special Issue entitled 'Glutamate Receptor-Dependent Synaptic Plasticity'. PMID:23439383

  15. Photoprotective effects of apple peel nanoparticles

    PubMed Central

    Bennet, Devasier; Kang, Se Chan; Gang, Jongback; Kim, Sanghyo

    2014-01-01

    Plants contain enriched bioactive molecules that can protect against skin diseases. Bioactive molecules become unstable and ineffective due to unfavorable conditions. In the present study, to improve the therapeutic efficacy of phytodrugs and enhance photoprotective capability, we used poly(D,L-lactide-co-glycolide) as a carrier of apple peel ethanolic extract (APETE) on permeation-enhanced nanoparticles (nano-APETE). The in vitro toxicity of nano-APETE-treated dermal fibroblast cells were studied in a bioimpedance system, and the results coincided with the viability assay. In addition, the continuous real-time evaluations of photodamage and photoprotective effect of nano-APETE on cells were studied. Among three different preparations of nano-APETE, the lowest concentration provided small, spherical, monodispersed, uniform particles which show high encapsulation, enhanced uptake, effective scavenging, and sustained intracellular delivery. Also, the nano-APETE is more flexible, allowing it to permeate through skin lipid membrane and release the drug in a sustained manner, thus confirming its ability as a sustained transdermal delivery. In summary, 50 μM nano-APETE shows strong synergistic photoprotective effects, thus demonstrating its higher activity on target sites for the treatment of skin damage, and would be of broad interest in the field of skin therapeutics. PMID:24379668

  16. Photoprotective effects of apple peel nanoparticles.

    PubMed

    Bennet, Devasier; Kang, Se Chan; Gang, Jongback; Kim, Sanghyo

    2014-01-01

    Plants contain enriched bioactive molecules that can protect against skin diseases. Bioactive molecules become unstable and ineffective due to unfavorable conditions. In the present study, to improve the therapeutic efficacy of phytodrugs and enhance photoprotective capability, we used poly(D,L-lactide-co-glycolide) as a carrier of apple peel ethanolic extract (APETE) on permeation-enhanced nanoparticles (nano-APETE). The in vitro toxicity of nano-APETE-treated dermal fibroblast cells were studied in a bioimpedance system, and the results coincided with the viability assay. In addition, the continuous real-time evaluations of photodamage and photoprotective effect of nano-APETE on cells were studied. Among three different preparations of nano-APETE, the lowest concentration provided small, spherical, monodispersed, uniform particles which show high encapsulation, enhanced uptake, effective scavenging, and sustained intracellular delivery. Also, the nano-APETE is more flexible, allowing it to permeate through skin lipid membrane and release the drug in a sustained manner, thus confirming its ability as a sustained transdermal delivery. In summary, 50 μM nano-APETE shows strong synergistic photoprotective effects, thus demonstrating its higher activity on target sites for the treatment of skin damage, and would be of broad interest in the field of skin therapeutics. PMID:24379668

  17. Pick-up, impact, and peeling

    NASA Astrophysics Data System (ADS)

    Singh, Harmeet; Hanna, James

    We consider a class of problems involving a one-dimensional, inextensible body with a propagating discontinuity (shock) associated with partial contact with a rigid obstacle providing steric, frictional, or adhesive forces. This class includes the pick-up and impact of an axially flowing string or cable, and the peeling of an adhesive tape. The dynamics are derived by applying an action principle to a non-material volume. The resulting boundary conditions provide momentum and energy jump conditions at the shock. These are combined with kinematic conditions on velocities and accelerations to obtain families of steady-state solutions parameterized by the shock velocity and momentum and energy sources. We find relationships between the jump in stress, injection of momentum, and dissipation of energy, which we apply to specific cases, and compare with other results in the literature on chain fountains, falling folded chains, and impulsively loaded cables. Time permitting, we will briefly discuss the possibility of using kinematic conditions and information about accelerating or otherwise unsteady forms of the adjoining bulk solutions to construct an equation of motion of the shock.

  18. Volumetric depth peeling for medical image display

    NASA Astrophysics Data System (ADS)

    Borland, David; Clarke, John P.; Fielding, Julia R.; TaylorII, Russell M.

    2006-01-01

    Volumetric depth peeling (VDP) is an extension to volume rendering that enables display of otherwise occluded features in volume data sets. VDP decouples occlusion calculation from the volume rendering transfer function, enabling independent optimization of settings for rendering and occlusion. The algorithm is flexible enough to handle multiple regions occluding the object of interest, as well as object self-occlusion, and requires no pre-segmentation of the data set. VDP was developed as an improvement for virtual arthroscopy for the diagnosis of shoulder-joint trauma, and has been generalized for use in other simple and complex joints, and to enable non-invasive urology studies. In virtual arthroscopy, the surfaces in the joints often occlude each other, allowing limited viewpoints from which to evaluate these surfaces. In urology studies, the physician would like to position the virtual camera outside the kidney collecting system and see inside it. By rendering invisible all voxels between the observer's point of view and objects of interest, VDP enables viewing from unconstrained positions. In essence, VDP can be viewed as a technique for automatically defining an optimal data- and task-dependent clipping surface. Radiologists using VDP display have been able to perform evaluations of pathologies more easily and more rapidly than with clinical arthroscopy, standard volume rendering, or standard MRI/CT slice viewing.

  19. Phenols in citrus peel byproducts. Concentrations of hydroxycinnamates and polymethoxylated flavones in citrus peel molasses.

    PubMed

    Manthey, J A; Grohmann, K

    2001-07-01

    In addition to the main flavanone glycosides (i.e., hesperidin and naringin) in citrus peel, polymethoxylated flavones and numerous hydroxycinnamates also occur and are major phenolic constituents of the molasses byproduct generated from fruit processing. Although a small number of the hydroxycinnamates in citrus occur as amides, most occur as esters and are susceptible to alkaline hydrolysis. This susceptibility to alkaline hydrolysis was used in measuring the concentrations of hydroxycinnamates in citrus peel molasses. The highest concentrations of hydroxycinnamates occurred in molasses of orange [C. sinensis (L.) Osbeck] and tangerine (C. reticulata Blanco.) compared to grapefruit (C. paradisi Macf.) and lemon [C. limon (L.) Burm.]. Concentrations of two phenolic glucosides, phlorin (phloroglucinol-beta-O-glucoside) and coniferin (coniferyl alcohol-4-beta-O-glucoside), were also measured. Measurements of the polymethoxylated flavones in molasses from several tangerine and orange varieties showed that these compounds occurred in the highest amounts in Dancy tangerine, whereas samples from two other tangerine molasses contained significantly lower levels, similar to those in the molasses samples from late- and early/mid-season oranges. PMID:11453761

  20. Extracts of black bean peel and pomegranate peel ameliorate oxidative stress-induced hyperglycemia in mice.

    PubMed

    Wang, Jian-Yun; Zhu, Chuang; Qian, Tian-Wei; Guo, Hao; Wang, Dong-Dong; Zhang, Fan; Yin, Xiaoxing

    2015-01-01

    Oxidative stress has a central role in the progression of diabetes mellitus (DM), which can directly result in the injury of islet β cells and consequent hyperglycemia. The aim of the present study was to evaluate the possible protective effects of black bean peel extract (BBPE), pomegranate peel extract (PPE) and a combination of the two (PPE + BBPE) on streptozotocin-induced DM mice. Oxidative stress was assessed by the levels of total antioxidative capability and glutathione in the serum. Fasting blood glucose and insulin levels, as well as the pancreas weight index and the histological changes in the pancreas, were also determined. The results showed that, after fours weeks of treatment with PPE, BBPE or PPE + BBPE, DM mice showed, to different degrees, a decrease in blood glucose, increases in insulin secretion and the pancreas weight index, and an increase in antioxidative activity. These changes were particularly evident in the DM mice subjected to the combined intervention strategy of PPE + BBPE. The histological findings indicated that the injury to the pancreatic islets in DM mice was also ameliorated following treatment. In conclusion, PPE and BBPE, particularly the combination of the two, have the ability to ameliorate hyperglycemia by inhibiting oxidative stress-induced pancreatic damage; this finding may be useful in the prevention and treatment of DM. PMID:25452774

  1. Stationary wavelet transform and higher order statistical analyses of intrafascicular nerve recordings.

    PubMed

    Qiao, Shaoyu; Torkamani-Azar, Mastaneh; Salama, Paul; Yoshida, Ken

    2012-10-01

    Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.

  2. Extracting information in spike time patterns with wavelets and information theory.

    PubMed

    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

    2015-02-01

    We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information.

  3. Velocity and Object Detection Using Quaternion Wavelets

    SciTech Connect

    Traversoni, Leonardo; Xu Yi

    2007-09-06

    DStarting from stereoscopic films we detect corresponding objects in both and stablish an epipolar geometry as well as corresponding moving objects are detected and its movement described all using quaternion wavelets and quaternion phase space decomposition.

  4. Wavelet analysis for characterizing human electroencephalogram signals

    NASA Astrophysics Data System (ADS)

    Li, Bai-Lian; Wu, Hsin-i.

    1995-04-01

    Wavelet analysis is a recently developed mathematical theory and computational method for decomposing a nonstationary signal into components that have good localization properties both in time and frequency domains and hierarchical structures. Wavelet transform provides local information and multiresolution decomposition on a signal that cannot be obtained using traditional methods such as Fourier transforms and distribution-based statistical methods. Hence the change in complex biological signals can be detected. We use wavelet analysis as an innovative method for identifying and characterizing multiscale electroencephalogram signals in this paper. We develop a wavelet-based stationary phase transition method to extract instantaneous frequencies of the signal that vary in time. The results under different clinical situations show that the brian triggers small bursts of either low or high frequency immediately prior to changing on the global scale to that behavior. This information could be used as a diagnostic for detecting the onset of an epileptic seizure.

  5. Wavelet-based acoustic recognition of aircraft

    SciTech Connect

    Dress, W.B.; Kercel, S.W.

    1994-09-01

    We describe a wavelet-based technique for identifying aircraft from acoustic emissions during take-off and landing. Tests show that the sensor can be a single, inexpensive hearing-aid microphone placed close to the ground the paper describes data collection, analysis by various technique, methods of event classification, and extraction of certain physical parameters from wavelet subspace projections. The primary goal of this paper is to show that wavelet analysis can be used as a divide-and-conquer first step in signal processing, providing both simplification and noise filtering. The idea is to project the original signal onto the orthogonal wavelet subspaces, both details and approximations. Subsequent analysis, such as system identification, nonlinear systems analysis, and feature extraction, is then carried out on the various signal subspaces.

  6. Applications of a fast continuous wavelet transform

    NASA Astrophysics Data System (ADS)

    Dress, William B.

    1997-04-01

    A fast, continuous, wavelet transform, justified by appealing to Shannon's sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and from the standard treatment of the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon's sampling theorem lets us view the Fourier transform of the data set as representing the continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time-domain sampling of the signal under analysis. Although more computationally costly and not represented by an orthogonal basis, the inherent flexibility and shift invariance of the frequency-space wavelets are advantageous for certain applications. The method has been applied to forensic audio reconstruction, speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants' heart beats. Audio reconstruction is aided by selection of desired regions in the 2D representation of the magnitude of the transformed signals. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass- spring system by an occupant's beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, different features may be extracted from voice

  7. Wavelet neural networks for stock trading

    NASA Astrophysics Data System (ADS)

    Zheng, Tianxing; Fataliyev, Kamaladdin; Wang, Lipo

    2013-05-01

    This paper explores the application of a wavelet neural network (WNN), whose hidden layer is comprised of neurons with adjustable wavelets as activation functions, to stock prediction. We discuss some basic rationales behind technical analysis, and based on which, inputs of the prediction system are carefully selected. This system is tested on Istanbul Stock Exchange National 100 Index and compared with traditional neural networks. The results show that the WNN can achieve very good prediction accuracy.

  8. Texture preservation in de-noising UAV surveillance video through multi-frame sampling

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Fevig, Ronald A.; Schultz, Richard R.

    2009-02-01

    Image de-noising is a widely-used technology in modern real-world surveillance systems. Methods can seldom do both de-noising and texture preservation very well without a direct knowledge of the noise model. Most of the neighborhood fusion-based de-noising methods tend to over-smooth the images, which causes a significant loss of detail. Recently, a new non-local means method has been developed, which is based on the similarities among the different pixels. This technique results in good preservation of the textures; however, it also causes some artifacts. In this paper, we utilize the scale-invariant feature transform (SIFT) [1] method to find the corresponding region between different images, and then reconstruct the de-noised images by a weighted sum of these corresponding regions. Both hard and soft criteria are chosen in order to minimize the artifacts. Experiments applied to real unmanned aerial vehicle thermal infrared surveillance video show that our method is superior to popular methods in the literature.

  9. Incrementing data quality of multi-frequency echograms using the Adaptive Wiener Filter (AWF) denoising algorithm

    NASA Astrophysics Data System (ADS)

    Peña, M.

    2016-10-01

    Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.

  10. Fast and Memory-Efficient Topological Denoising of 2D and 3D Scalar Fields.

    PubMed

    Günther, David; Jacobson, Alec; Reininghaus, Jan; Seidel, Hans-Peter; Sorkine-Hornung, Olga; Weinkauf, Tino

    2014-12-01

    Data acquisition, numerical inaccuracies, and sampling often introduce noise in measurements and simulations. Removing this noise is often necessary for efficient analysis and visualization of this data, yet many denoising techniques change the minima and maxima of a scalar field. For example, the extrema can appear or disappear, spatially move, and change their value. This can lead to wrong interpretations of the data, e.g., when the maximum temperature over an area is falsely reported being a few degrees cooler because the denoising method is unaware of these features. Recently, a topological denoising technique based on a global energy optimization was proposed, which allows the topology-controlled denoising of 2D scalar fields. While this method preserves the minima and maxima, it is constrained by the size of the data. We extend this work to large 2D data and medium-sized 3D data by introducing a novel domain decomposition approach. It allows processing small patches of the domain independently while still avoiding the introduction of new critical points. Furthermore, we propose an iterative refinement of the solution, which decreases the optimization energy compared to the previous approach and therefore gives smoother results that are closer to the input. We illustrate our technique on synthetic and real-world 2D and 3D data sets that highlight potential applications. PMID:26356972

  11. Subject-specific patch-based denoising for contrast-enhanced cardiac MR images

    NASA Astrophysics Data System (ADS)

    Ma, Lorraine; Ebrahimi, Mehran; Pop, Mihaela

    2016-03-01

    Many patch-based techniques in imaging, e.g., Non-local means denoising, require tuning parameters to yield optimal results. In real-world applications, e.g., denoising of MR images, ground truth is not generally available and the process of choosing an appropriate set of parameters is a challenge. Recently, Zhu et al. proposed a method to define an image quality measure, called Q, that does not require ground truth. In this manuscript, we evaluate the effect of various parameters of the NL-means denoising on this quality metric Q. Our experiments are based on the late-gadolinium enhancement (LGE) cardiac MR images that are inherently noisy. Our described exhaustive evaluation approach can be used in tuning parameters of patch-based schemes. Even in the case that an estimation of optimal parameters is provided using another existing approach, our described method can be used as a secondary validation step. Our preliminary results suggest that denoising parameters should be case-specific rather than generic.

  12. a Universal De-Noising Algorithm for Ground-Based LIDAR Signal

    NASA Astrophysics Data System (ADS)

    Ma, Xin; Xiang, Chengzhi; Gong, Wei

    2016-06-01

    Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

  13. Group-sparse representation with dictionary learning for medical image denoising and fusion.

    PubMed

    Li, Shutao; Yin, Haitao; Fang, Leyuan

    2012-12-01

    Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

  14. A multi-scale non-local means algorithm for image de-noising

    NASA Astrophysics Data System (ADS)

    Nercessian, Shahan; Panetta, Karen A.; Agaian, Sos S.

    2012-06-01

    A highly studied problem in image processing and the field of electrical engineering in general is the recovery of a true signal from its noisy version. Images can be corrupted by noise during their acquisition or transmission stages. As noisy images are visually very poor in quality, and complicate further processing stages of computer vision systems, it is imperative to develop algorithms which effectively remove noise in images. In practice, it is a difficult task to effectively remove the noise while simultaneously retaining the edge structures within the image. Accordingly, many de-noising algorithms have been considered attempt to intelligent smooth the image while still preserving its details. Recently, a non-local means (NLM) de-noising algorithm was introduced, which exploited the redundant nature of images to achieve image de-noising. The algorithm was shown to outperform current de-noising standards, including Gaussian filtering, anisotropic diffusion, total variation minimization, and multi-scale transform coefficient thresholding. However, the NLM algorithm was developed in the spatial domain, and therefore, does not leverage the benefit that multi-scale transforms provide a framework in which signals can be better distinguished by noise. Accordingly, in this paper, a multi-scale NLM (MS-NLM) algorithm is proposed, which combines the advantage of the NLM algorithm and multi-scale image processing techniques. Experimental results via computer simulations illustrate that the MS-NLM algorithm outperforms the NLM, both visually and quantitatively.

  15. Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising

    PubMed Central

    Ai, Danni; Yang, Jian; Fan, Jingfan; Cong, Weijian; Wang, Yongtian

    2015-01-01

    Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods. PMID:25993566

  16. Fuzzy logic recursive change detection for tracking and denoising of video sequences

    NASA Astrophysics Data System (ADS)

    Zlokolica, Vladimir; De Geyter, Matthias; Schulte, Stefan; Pizurica, Aleksandra; Philips, Wilfried; Kerre, Etienne

    2005-03-01

    In this paper we propose a fuzzy logic recursive scheme for motion detection and temporal filtering that can deal with the Gaussian noise and unsteady illumination conditions both in temporal and spatial direction. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection in order to determine the degree of motion confidence. The proposed motion detector combines the membership degree appropriately using defined fuzzy rules, where the membership degree of motion for each pixel in a 2D-sliding-window is determined by the proposed membership function. Both fuzzy membership function and fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We perform simultaneously tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence with respect to the existence of motion. Finally, temporally filtered frames are further processed by the proposed spatial filter in order to obtain denoised image sequence. The main contribution of this paper is the robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: robustness to noise and changing illumination conditions and motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.

  17. Denoising of hyperspectral images by best multilinear rank approximation of a tensor

    NASA Astrophysics Data System (ADS)

    Marin-McGee, Maider; Velez-Reyes, Miguel

    2010-04-01

    The hyperspectral image cube can be modeled as a three dimensional array. Tensors and the tools of multilinear algebra provide a natural framework to deal with this type of mathematical object. Singular value decomposition (SVD) and its variants have been used by the HSI community for denoising of hyperspectral imagery. Denoising of HSI using SVD is achieved by finding a low rank approximation of a matrix representation of the hyperspectral image cube. This paper investigates similar concepts in hyperspectral denoising by using a low multilinear rank approximation the given HSI tensor representation. The Best Multilinear Rank Approximation (BMRA) of a given tensor A is to find a lower multilinear rank tensor B that is as close as possible to A in the Frobenius norm. Different numerical methods to compute the BMRA using Alternating Least Square (ALS) method and Newton's Methods over product of Grassmann manifolds are presented. The effect of the multilinear rank, the numerical method used to compute the BMRA, and different parameter choices in those methods are studied. Results show that comparable results are achievable with both ALS and Newton type methods. Also, classification results using the filtered tensor are better than those obtained either with denoising using SVD or MNF.

  18. Denoising peptide tandem mass spectra for spectral libraries: a Bayesian approach.

    PubMed

    Shao, Wenguang; Lam, Henry

    2013-07-01

    With the rapid accumulation of data from shotgun proteomics experiments, it has become feasible to build comprehensive and high-quality spectral libraries of tandem mass spectra of peptides. A spectral library condenses experimental data into a retrievable format and can be used to aid peptide identification by spectral library searching. A key step in spectral library building is spectrum denoising, which is best accomplished by merging multiple replicates of the same peptide ion into a consensus spectrum. However, this approach cannot be applied to "singleton spectra," for which only one observed spectrum is available for the peptide ion. We developed a method, based on a Bayesian classifier, for denoising peptide tandem mass spectra. The classifier accounts for relationships between peaks, and can be trained on the fly from consensus spectra and immediately applied to denoise singleton spectra, without hard-coded knowledge about peptide fragmentation. A linear regression model was also trained to predict the number of useful "signal" peaks in a spectrum, thereby obviating the need for arbitrary thresholds for peak filtering. This Bayesian approach accumulates weak evidence systematically to boost the discrimination power between signal and noise peaks, and produces readily interpretable conditional probabilities that offer valuable insights into peptide fragmentation behaviors. By cross validation, spectra denoised by this method were shown to retain more signal peaks, and have higher spectral similarities to replicates, than those filtered by intensity only.

  19. Recovery of Ga(III) by Raw and Alkali Treated Citrus limetta Peels

    PubMed Central

    2014-01-01

    Alkali treated Citrus limetta peels were used for recovery of Ga(III) from its aqueous solution. The raw and alkali treated peels were characterized for functional groups. The efficiency of adsorption increased from 47.62 mg/g for raw peels to 83.33 mg/g for alkali treated peels. Between pH 1 and 3, the adsorption increased and thereafter decreased drastically. The adsorption followed pseudosecond order kinetics and Langmuir isotherm gave the best fit for the experimental data. Desorption studies showed 95.28% desorption after 3 cycles for raw peels while it was 89.51% for alkali treated peels. Simulated Bayer liquor showed 39.57% adsorption for gallium ions on raw peels which was enhanced to 41.13% for alkali treated peels. PMID:27382624

  20. The Continuous wavelet in airborne gravimetry

    NASA Astrophysics Data System (ADS)

    Liang, X.; Liu, L.

    2013-12-01

    Airborne gravimetry is an efficient method to recover medium and high frequency band of earth gravity over any region, especially inaccessible areas, which can measure gravity data with high accuracy,high resolution and broad range in a rapidly and economical way, and It will play an important role for geoid and geophysical exploration. Filtering methods for reducing high-frequency errors is critical to the success of airborne gravimetry due to Aircraft acceleration determination based on GPS.Tradiontal filters used in airborne gravimetry are FIR,IIR filer and so on. This study recommends an improved continuous wavelet to process airborne gravity data. Here we focus on how to construct the continuous wavelet filters and show their working principle. Particularly the technical parameters (window width parameter and scale parameter) of the filters are tested. Then the raw airborne gravity data from the first Chinese airborne gravimetry campaign are filtered using FIR-low pass filter and continuous wavelet filters to remove the noise. The comparison to reference data is performed to determinate external accuracy, which shows that continuous wavelet filters applied to airborne gravity in this thesis have good performances. The advantages of the continuous wavelet filters over digital filters are also introduced. The effectiveness of the continuous wavelet filters for airborne gravimetry is demonstrated through real data computation.

  1. Pomegranate peel pectin films as affected by montmorillonite.

    PubMed

    Oliveira, Túlio Ítalo S; Zea-Redondo, Luna; Moates, Graham K; Wellner, Nikolaus; Cross, Kathryn; Waldron, Keith W; Azeredo, Henriette M C

    2016-05-01

    The industrial production of pomegranate juice has been favored by its alleged health benefits derived from its antioxidant properties. The processing of pomegranate juice involves squeezing juice from the fruit with the seeds and the peels together, leaving a pomace consisting of approximately 73 wt% peels. In this study, pectin was extracted from pomegranate peels, and used to produce films with different contents of montmorillonite (MMT) as a nanoreinforcement material. The nanoreinforcement improved the tensile strength and modulus of films when added at up to 6 wt%, while the further addition of MMT (to 8 wt%) reduced the reinforcement effect, probably because of dispersion problems. The elongation was decreased with increasing MMT concentrations. The water vapor permeability decreased with increasing MMT contents up to 8 wt% MMT, indicating that the increased tortuosity of the permeant path was effective on barrier properties of the film. PMID:26769511

  2. Peeled film GaAs solar cell development

    NASA Technical Reports Server (NTRS)

    Wilt, D. M.; Thomas, R. D.; Bailey, S. G.; Brinker, D. J.; Deangelo, F. L.

    1990-01-01

    Thin-film, single-crystal gallium arsenide (GaAs) solar cells could exhibit a specific power approaching 700 W/kg including coverglass. A simple process has been described whereby epitaxial GaAs layers are peeled from a reusable substrate. This process takes advantage of the extreme selectivity of the etching rate of aluminum arsenide (AlAs) over GaAs in dilute hydrofluoric acid. The feasibility of using the peeled film technique to fabricate high-efficiency, low-mass GaAs solar cells is presently demonstrated. A peeled film GaAs solar cell was successfully produced. The device, although fractured and missing the aluminum gallium arsenide window and antireflective coating, had a Voc of 874 mV and a fill factor of 68 percent under AM0 illumination.

  3. Partial identification of antifungal compounds from Punica granatum peel extracts.

    PubMed

    Glazer, Ira; Masaphy, Segula; Marciano, Prosper; Bar-Ilan, Igal; Holland, Doron; Kerem, Zohar; Amir, Rachel

    2012-05-16

    Aqueous extracts of pomegranate peels were assayed in vitro for their antifungal activity against six rot fungi that cause fruit and vegetable decay during storage. The growth rates of Alternaria alternata , Stemphylium botryosum , and Fusarium spp. were significantly inhibited by the extracts. The growth rates were negatively correlated with the levels of total polyphenolic compounds in the extract and particularly with punicalagins, the major ellagitannins in pomegranate peels. Ellagitannins were also found to be the main compounds in the bioactive fractions using bioautograms, and punicalagins were identified as the main bioactive compounds using chromatographic separation. These results suggest that ellagitannins, and more specifically punicalagins, which are the dominant compounds in pomegranate peels, may be used as a control agent of storage diseases and to reduce the use of synthetic fungicides. PMID:22533815

  4. Generation of ultra-sound during tape peeling

    NASA Astrophysics Data System (ADS)

    Marston, Jeremy O.; Riker, Paul W.; Thoroddsen, Sigurdur T.

    2014-03-01

    We investigate the generation of the screeching sound commonly heard during tape peeling using synchronised high-speed video and audio acquisition. We determine the peak frequencies in the audio spectrum and, in addition to a peak frequency at the upper end of the audible range (around 20 kHz), we find an unexpected strong sound with a high-frequency far above the audible range, typically around 50 kHz. Using the corresponding video data, the origins of the key frequencies are confirmed as being due to the substructure ``fracture'' bands, which we herein observe in both high-speed continuous peeling motions and in the slip phases for stick-slip peeling motions.

  5. Generation of ultra-sound during tape peeling.

    PubMed

    Marston, Jeremy O; Riker, Paul W; Thoroddsen, Sigurdur T

    2014-01-01

    We investigate the generation of the screeching sound commonly heard during tape peeling using synchronised high-speed video and audio acquisition. We determine the peak frequencies in the audio spectrum and, in addition to a peak frequency at the upper end of the audible range (around 20 kHz), we find an unexpected strong sound with a high-frequency far above the audible range, typically around 50 kHz. Using the corresponding video data, the origins of the key frequencies are confirmed as being due to the substructure "fracture" bands, which we herein observe in both high-speed continuous peeling motions and in the slip phases for stick-slip peeling motions. PMID:24651648

  6. Inhibition of microbial pathogens using fruit and vegetable peel extracts.

    PubMed

    Rakholiya, Kalpna; Kaneria, Mital; Chanda, Sumitra

    2014-09-01

    The aim of the present work is to evaluate the antimicrobial potency of some vegetable and fruit peels. The extraction was done by individual cold percolation method using various solvents with increasing polarity (Hexane, ethyl acetate, acetone, methanol and aqueous). The antimicrobial activity was done by agar well diffusion assay against five Gram positive bacteria, five Gram negative bacteria and four fungi. All extracts demonstrated varied level of antimicrobial activity. The peel extracts showed highest zone of inhibition against Gram negative bacteria as compared to Gram positive bacteria and fungi. Amongst studied peel extracts Citrus limon followed by Manilkara zapota and Carica papaya showed good antimicrobial activity indicating its potency as a promising source of natural antimicrobics. The results confirm the belief that agro waste can be therapeutically used. PMID:24725235

  7. Speckle reduction process based on digital filtering and wavelet compounding in optical coherence tomography for dermatology

    NASA Astrophysics Data System (ADS)

    Gómez Valverde, Juan J.; Ortuño, Juan E.; Guerra, Pedro; Hermann, Boris; Zabihian, Behrooz; Rubio-Guivernau, José L.; Santos, Andrés.; Drexler, Wolfgang; Ledesma-Carbayo, Maria J.

    2015-07-01

    Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we propose a new speckle reduction process and compare it with various denoising filters with high edge-preserving potential, using several sets of dermatological OCT B-scans. To validate the performance we used a custom-designed spectral domain OCT and two different data set groups. The first group consisted in five datasets of a single B-scan captured N times (with N<20), the second were five 3D volumes of 25 Bscans. As quality metrics we used signal to noise (SNR), contrast to noise (CNR) and equivalent number of looks (ENL) ratios. Our results show that a process based on a combination of a 2D enhanced sigma digital filter and a wavelet compounding method achieves the best results in terms of the improvement of the quality metrics. In the first group of individual B-scans we achieved improvements in SNR, CNR and ENL of 16.87 dB, 2.19 and 328 respectively; for the 3D volume datasets the improvements were 15.65 dB, 3.44 and 1148. Our results suggest that the proposed enhancement process may significantly reduce speckle, increasing SNR, CNR and ENL and reducing the number of extra acquisitions of the same frame.

  8. An improved automated ultrasonic NDE system by wavelet and neuron networks.

    PubMed

    Bettayeb, Fairouz; Rachedi, Tarek; Benbartaoui, Hamid

    2004-04-01

    Despite of the widespread and increasing use of digitized signals, the ultrasonic testing community has not realized yet the full potential of the electronic processing. The performance of an ultrasonic flaw detection method is evaluated by the success of distinguishing the flaw echoes from those scattered by microstructures. So, de-noising of ultrasonic signals is extremely important as to correctly identify smaller defects, because the probability of detection usually decreases as the defect size decreases, while the probability of false call does increase. In this paper, the wavelet transform has been successfully experimented to suppress noise and to enhance flaw location from ultrasonic signal, with a good defect localization. The obtained result is then directed to an automatic Artificial Neuronal Networks classification and learning algorithm of defects from A-scan data. Since there is some uncertainty connected with the testing technique, the system needs a numerical modelling. So, knowing the technical characteristics of the transducer, we can preview which are the defects that experimental inspection should find. Indeed, the system performs simulation of the ultrasonic wave propagation in the material, and gives a very helpful tool to get information and physical phenomena understanding, which can help to a suitable prediction of the service life of the component.

  9. Demonstration tests of infrared peeling system with electrical emitters for tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Infrared (IR) dry-peeling is an emerging technology that could avoid the drawbacks of steam and lye peeling of tomatoes. The objectives of this research was to evaluate the performance of an IR peeling system at two tomato processing plants located in California and to compare product quality, peela...

  10. Analysis of the phenolic compounds in longan (Dimocarpus longan lour.) peel

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Longan fruit are susceptible to chilling injury, where the injured peel exhibits discoloration due to water-soaking and enzymatic browning. This peel discoloration is dependent to a large degree on the composition of the phenolic compounds. Yet, the main classes of phenols in longan peel remain la...

  11. Apple peels--a versatile biomass for water purification?

    PubMed

    Mallampati, Ramakrishna; Valiyaveettil, Suresh

    2013-05-22

    The presence of anions such as chromate, arsenate, and arsenite in drinking water is a major health concern in many parts of the world due to their high toxicity. Removal of such anions from water using low cost biomass is an efficient and affordable treatment process. Owing to the easy availability and biodegradability, we chose to use apple peel as a substrate for our investigations. Zirconium cations were immobilized onto the apple peel surface and used for the extraction of anions. Zirconium loaded apple peels were used to extract anions such as phosphate, arsenate, arsenite, and chromate ions from aqueous solutions. The presence of Zr cations on the apple peel surface was characterized using XPS. The modified adsorbent was characterized using SEM, EDS, and FT-IR. Zr treated apple peels showed efficient adsorption toward AsO2(-) (15.64 mg/g), AsO4(3-) (15.68 mg/g), Cr2O7(2-) (25.28 mg/g), and PO4(3-) (20.35 mg/g) anions. The adsorption and desorption studies revealed the adsorption mechanism involves electrostatic interactions. Anion removal efficiency was estimated by batch adsorption studies. Adsorption kinetic parameters for all anions at different concentrations were described using pseudo-first-order and pseudo-second-order rate equations. Langumir and Freundlich isotherms were used to validate our adsorption data. Arsenate and chromate anions were strongly adsorbed at the pH range from 2 to 6, while arsenite was extracted efficiently between pH 9 and 10. Overall, the Zr immobilized apple peel is an efficient adsorbent for common anionic pollutants.

  12. Multiresolution With Super-Compact Wavelets

    NASA Technical Reports Server (NTRS)

    Lee, Dohyung

    2000-01-01

    The solution data computed from large scale simulations are sometimes too big for main memory, for local disks, and possibly even for a remote storage disk, creating tremendous processing time as well as technical difficulties in analyzing the data. The excessive storage demands a corresponding huge penalty in I/O time, rendering time and transmission time between different computer systems. In this paper, a multiresolution scheme is proposed to compress field simulation or experimental data without much loss of important information in the representation. Originally, the wavelet based multiresolution scheme was introduced in image processing, for the purposes of data compression and feature extraction. Unlike photographic image data which has rather simple settings, computational field simulation data needs more careful treatment in applying the multiresolution technique. While the image data sits on a regular spaced grid, the simulation data usually resides on a structured curvilinear grid or unstructured grid. In addition to the irregularity in grid spacing, the other difficulty is that the solutions consist of vectors instead of scalar values. The data characteristics demand more restrictive conditions. In general, the photographic images have very little inherent smoothness with discontinuities almost everywhere. On the other hand, the numerical solutions have smoothness almost everywhere and discontinuities in local areas (shock, vortices, and shear layers). The wavelet bases should be amenable to the solution of the problem at hand and applicable to constraints such as numerical accuracy and boundary conditions. In choosing a suitable wavelet basis for simulation data among a variety of wavelet families, the supercompact wavelets designed by Beam and Warming provide one of the most effective multiresolution schemes. Supercompact multi-wavelets retain the compactness of Haar wavelets, are piecewise polynomial and orthogonal, and can have arbitrary order of

  13. Automated quantitative analysis of in-situ NaI measured spectra in the marine environment using a wavelet-based smoothing technique.

    PubMed

    Tsabaris, Christos; Prospathopoulos, Aristides

    2011-10-01

    An algorithm for automated analysis of in-situ NaI γ-ray spectra in the marine environment is presented. A standard wavelet denoising technique is implemented for obtaining a smoothed spectrum, while the stability of the energy spectrum is achieved by taking advantage of the permanent presence of two energy lines in the marine environment. The automated analysis provides peak detection, net area calculation, energy autocalibration, radionuclide identification and activity calculation. The results of the algorithm performance, presented for two different cases, show that analysis of short-term spectra with poor statistical information is considerably improved and that incorporation of further advancements could allow the use of the algorithm in early-warning marine radioactivity systems. PMID:21742510

  14. Multiparameter radar analysis using wavelets

    NASA Astrophysics Data System (ADS)

    Tawfik, Ben Bella Sayed

    Multiparameter radars have been used in the interpretation of many meteorological phenomena. Rainfall estimates can be obtained from multiparameter radar measurements. Studying and analyzing spatial variability of different rainfall algorithms, namely R(ZH), the algorithm based on reflectivity, R(ZH, ZDR), the algorithm based on reflectivity and differential reflectivity, R(KDP), the algorithm based on specific differential phase, and R(KDP, Z DR), the algorithm based on specific differential phase and differential reflectivity, are important for radar applications. The data used in this research were collected using CSU-CHILL, CP-2, and S-POL radars. In this research multiple objectives are addressed using wavelet analysis namely, (1)space time variability of various rainfall algorithms, (2)separation of convective and stratiform storms based on reflectivity measurements, (3)and detection of features such as bright bands. The bright band is a multiscale edge detection problem. In this research, the technique of multiscale edge detection is applied on the radar data collected using CP-2 radar on August 23, 1991 to detect the melting layer. In the analysis of space/time variability of rainfall algorithms, wavelet variance introduces an idea about the statistics of the radar field. In addition, multiresolution analysis of different rainfall estimates based on four algorithms, namely R(ZH), R( ZH, ZDR), R(K DP), and R(KDP, Z DR), are analyzed. The flood data of July 29, 1997 collected by CSU-CHILL radar were used for this analysis. Another set of S-POL radar data collected on May 2, 1997 at Wichita, Kansas were used as well. At each level of approximation, the detail and the approximation components are analyzed. Based on this analysis, the rainfall algorithms can be judged. From this analysis, an important result was obtained. The Z-R algorithms that are widely used do not show the full spatial variability of rainfall. In addition another intuitively obvious result

  15. An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction

    NASA Astrophysics Data System (ADS)

    Chen, Yangkang; Huang, Weilin; Zhang, Dong; Chen, Wei

    2016-10-01

    Simultaneous seismic data denoising and reconstruction is a currently popular research subject in modern reflection seismology. Traditional rank-reduction based 3D seismic data denoising and reconstruction algorithm will cause strong residual noise in the reconstructed data and thus affect the following processing and interpretation tasks. In this paper, we propose an improved rank-reduction method by modifying the truncated singular value decomposition (TSVD) formula used in the traditional method. The proposed approach can help us obtain nearly perfect reconstruction performance even in the case of low signal-to-noise ratio (SNR). The proposed algorithm is tested via one synthetic and field data examples. Considering that seismic data interpolation and denoising source packages are seldom in the public domain, we also provide a program template for the rank-reduction based simultaneous denoising and reconstruction algorithm by providing an open-source Matlab package.

  16. The study of real-time denoising algorithm based on parallel computing for the MEMS IR imager

    NASA Astrophysics Data System (ADS)

    Gong, Cheng; Hui, Mei; Dong, Liquan; Zhao, Yuejin

    2011-11-01

    Recent years, the MEMS-based optical readout infrared imaging technology is becoming a research hotspot. Studies show that the MEMS-based optical readout infrared imager features a high frame rate. Considering the high data Throughput and computing complexity of denoising algorithm It's difficult to ensure real-time of the image processing. In order to improve processing speed and achieve real-time, we conducted a study of denoising algorithm based on parallel computing using FPGA (Field Programmable Gate Array). In the paper, we analyze the imaging characteristics of MEMS-based optical readout infrared imager and design parallel computing methods for real-time denoising using the hardware description language. The experiment shows that the parallel computing denoising algorithm can improve infrared image processing speed to meet real-time requirement.

  17. Tolerability and Efficacy of Retinoic Acid Given after Full-face Peel Treatment of Photodamaged Skin

    PubMed Central

    Hu, Judy Y.; Biron, Julie A.; Yatskayer, Margarita; Dahl, Amanda; Oresajo, Christian

    2011-01-01

    Objective: All-trans retinoic acid is a well-established topical treatment of photodamaged skin. This study assessed the tolerance and efficacy of all-trans retinoic acid after full-face treatment with a chemical peel. Design: This was a split-face, randomized study. One side of each face was treated with peel and the other side with peel and all-trans retinoic acid (3%). Four treatments were given during the 10-week study period. Setting: Physician office. Participants: Fifteen female subjects 39 to 55 years of age. Measurements: Results were evaluated at Baseline; Weeks 4, 7, and 10; and at a 13-week follow-up visit by dermal grading of visual symptoms of irritation, subjective experiences of irritation, clinical grading of skin condition, and self-assessment questionnaires. Results: Both peel and peel plus all-trans retinoic acid treatments achieved significant improvement in fine lines, radiance, roughness, skin tone clarity, skin tone evenness, and hyperpigmentation appearance. Improvement in wrinkles and firmness was not observed in the peel plus all-trans retinoic acid arm, while pore appearance failed to improve in either treatment arm. Improvement in overall facial appearance was greater in the peel alone arm. Peel alone and the addition of all-trans retinoic acid did not cause dryness, edema, or peeling, and the frequency of peel-induced erythema did not increase with the addition of all-trans retinoic acid. Subject-perceived improvements with the peel treatment did not differ significantly from subject-perceived improvements of the peel plus all-trans retinoic acid treatment. Adverse events requiring intervention or discontinuing treatment were not observed in either treatment arm. Conclusion: The addition of all-trans retinoic acid after peel treatment does not significantly enhance peel-induced improvement in photoaging parameters, peel-induced adverse effects, and subject-perceived improvements. PMID:22010055

  18. Wavelet transforms as solutions of partial differential equations

    SciTech Connect

    Zweig, G.

    1997-10-01

    This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Wavelet transforms are useful in representing transients whose time and frequency structure reflect the dynamics of an underlying physical system. Speech sound, pressure in turbulent fluid flow, or engine sound in automobiles are excellent candidates for wavelet analysis. This project focused on (1) methods for choosing the parent wavelet for a continuous wavelet transform in pattern recognition applications and (2) the more efficient computation of continuous wavelet transforms by understanding the relationship between discrete wavelet transforms and discretized continuous wavelet transforms. The most interesting result of this research is the finding that the generalized wave equation, on which the continuous wavelet transform is based, can be used to understand phenomena that relate to the process of hearing.

  19. Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury

    PubMed Central

    Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel’farb, Georgy; Ovechkin, Alexander

    2013-01-01

    Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals

  20. Image wavelet decomposition and applications

    NASA Technical Reports Server (NTRS)

    Treil, N.; Mallat, S.; Bajcsy, R.

    1989-01-01

    The general problem of computer vision has been investigated for more that 20 years and is still one of the most challenging fields in artificial intelligence. Indeed, taking a look at the human visual system can give us an idea of the complexity of any solution to the problem of visual recognition. This general task can be decomposed into a whole hierarchy of problems ranging from pixel processing to high level segmentation and complex objects recognition. Contrasting an image at different representations provides useful information such as edges. An example of low level signal and image processing using the theory of wavelets is introduced which provides the basis for multiresolution representation. Like the human brain, we use a multiorientation process which detects features independently in different orientation sectors. So, images of the same orientation but of different resolutions are contrasted to gather information about an image. An interesting image representation using energy zero crossings is developed. This representation is shown to be experimentally complete and leads to some higher level applications such as edge and corner finding, which in turn provides two basic steps to image segmentation. The possibilities of feedback between different levels of processing are also discussed.

  1. De-noising of microwave satellite soil moisture time series

    NASA Astrophysics Data System (ADS)

    Su, Chun-Hsu; Ryu, Dongryeol; Western, Andrew; Wagner, Wolfgang

    2013-04-01

    Technology) ASCAT data sets to identify two types of errors that are spectrally distinct. Based on a semi-empirical model of soil moisture dynamics, we consider possible digital filter designs to improve the accuracy of their soil moisture products by reducing systematic periodic errors and stochastic noise. We describe a methodology to design bandstop filters to remove artificial resonances, and a Wiener filter to remove stochastic white noise present in the satellite data. Utility of these filters is demonstrated by comparing de-noised data against in-situ observations from ground monitoring stations in the Murrumbidgee Catchment (Smith et al., 2012), southeast Australia. Albergel, C., de Rosnay, P., Gruhier, C., Muñoz Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y. H., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sensing of Environment, 118, 215-226. Scipal, K., Holmes, T., de Jeu, R., Naeimi, V., & Wagner, W. (2008), A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophysical Research Letters, 35, L24403. Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., & Richter, H. (2012). The Murrumbidgee soil moisture network data set. Water Resources Research, 48, W07701. Su, C.-H., Ryu, D., Young, R., Western, A. W., & Wagner, W. (2012). Inter-comparison of microwave satellite soil moisture retrievals over Australia. Submitted to Remote Sensing of Environment.

  2. Analysis of autostereoscopic three-dimensional images using multiview wavelets.

    PubMed

    Saveljev, Vladimir; Palchikova, Irina

    2016-08-10

    We propose that multiview wavelets can be used in processing multiview images. The reference functions for the synthesis/analysis of multiview images are described. The synthesized binary images were observed experimentally as three-dimensional visual images. The symmetric multiview B-spline wavelets are proposed. The locations recognized in the continuous wavelet transform correspond to the layout of the test objects. The proposed wavelets can be applied to the multiview, integral, and plenoptic images. PMID:27534470

  3. Wavelet based detection of manatee vocalizations

    NASA Astrophysics Data System (ADS)

    Gur, Berke M.; Niezrecki, Christopher

    2005-04-01

    The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. Several boater warning systems, based upon manatee vocalizations, have been proposed to reduce the number of collisions. Three detection methods based on the Fourier transform (threshold, harmonic content and autocorrelation methods) were previously suggested and tested. In the last decade, the wavelet transform has emerged as an alternative to the Fourier transform and has been successfully applied in various fields of science and engineering including the acoustic detection of dolphin vocalizations. As of yet, no prior research has been conducted in analyzing manatee vocalizations using the wavelet transform. Within this study, the wavelet transform is used as an alternative to the Fourier transform in detecting manatee vocalizations. The wavelet coefficients are analyzed and tested against a specified criterion to determine the existence of a manatee call. The performance of the method presented is tested on the same data previously used in the prior studies, and the results are compared. Preliminary results indicate that using the wavelet transform as a signal processing technique to detect manatee vocalizations shows great promise.

  4. Review of wavelet transforms for pattern recognitions

    NASA Astrophysics Data System (ADS)

    Szu, Harold H.

    1996-03-01

    After relating the adaptive wavelet transform to the human visual and hearing systems, we exploit the synergism between such a smart sensor processing with brain-style neural network computing. The freedom of choosing an appropriate kernel of a linear transform, which is given to us by the recent mathematical foundation of the wavelet transform, is exploited fully and is generally called the adaptive wavelet transform (WT). However, there are several levels of adaptivity: (1) optimum coefficients: adjustable transform coefficients chosen with respect to a fixed mother kernel for better invariant signal representation, (2) super-mother: grouping different scales of daughter wavelets of same or different mother wavelets at different shift location into a new family called a superposition mother kernel for better speech signal classification, (3) variational calculus to determine ab initio a constraint optimization mother for a specific task. The tradeoff between the mathematical rigor of the complete orthonormality and the speed of order (N) with the adaptive flexibility is finally up to the user's needs. Then, to illustrate (1), a new invariant optoelectronic architecture of a wedge- shape filter in the WT domain is given for scale-invariant signal classification by neural networks.

  5. Resist behaviour during peeling release in nano-imprint lithography

    NASA Astrophysics Data System (ADS)

    Chalvin, Florian; Nakamura, Naoto; Tochino, Takamitsu; Yasuda, Masaaki; Kawata, Hiroaki; Hirai, Yoshihiko

    2016-05-01

    In order to minimize the defects formation when using nano-imprinting process we investigated the efforts applied on the resist during the release of the template. Lift-off release has already been characterized accurately but for peeling studies are still lacking. However from experimental results it has been observed that peeling offers better performances when it comes to limit the defects. Using finite element method we simulated imprinting on PMMA resist by a silicon template and extracted the maximal release force and the induced stress in the resist in regard to the template stiffness and the number of patterns imprinted. Compared to lift-off method we found that maximal release force was much lower and decided to investigate the induced stress behaviour. We observed that using peeling the maximal release force doesn't increase linearly in function of the template size as in lift-off but instead saturates beyond a certain template size, that saturating point depending on the template stiffness, a low stiffness meaning a lower maximal release force. However we found an opposite trend when we extracted the induced stress in the resist which decreases as the template stiffness increases, theoretically resulting in fewer defects. This seems to be due to the smaller bending of the more rigid template that put less constraint on the imprinted features during the releasing and thus avoid breaking them in the process. Therefore according to these results, to minimize defects when peeling release method is employed we should use a highly rigid template.

  6. Radiography of nonaxisymmetric objects: An onion-peeling inversion method

    NASA Astrophysics Data System (ADS)

    Schwierz-Iosefzon, T.; Notea, A.; Deutsch, M.

    2002-09-01

    An onion-peeling method for obtaining the linear attenuation coefficient distribution within a body from a single radiographic projection is presented. Unlike previous methods, which are applicable only to axi- or centrosymmetric objects, ours requires only mirror symmetry relative to the plane of the radiograph. An example of the use of the method is presented and discussed.

  7. Extraction kinetics and properties of proanthocyanidins from pomegranate peel

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With an objective of developing a safe and efficient method to extract proanthocyanidins products from pomegranate peel for use in nutraceuticals or as food additives, the effects of extraction parameters on the production efficiency, product properties, and extraction kinetics were systematically s...

  8. Shelf life and microbial profile of peeled onions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The increased usage of peeled onions over the past ten years by food service operations and fast-food restaurants has been plagued by black mold decay during cold-chain storage. This study examined the epiphytic microbiological distribution on onions and what effects various processing steps have on...

  9. The pharmacokinetics and health benefits of orange peel compounds

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Orange peel is a resource rich in phenolic antioxidants, including several classes of flavonoids and hydroxycinnamates. These compounds have been extensively studied for their biological actions particularly against chronic diseases in humans. Yet, full development of these materials as new, commerc...

  10. Dietary fibre components and pectin chemical features of peels during ripening in banana and plantain varieties.

    PubMed

    Happi Emaga, Thomas; Robert, Christelle; Ronkart, Sébastien N; Wathelet, Bernard; Paquot, Michel

    2008-07-01

    The effects of the ripeness stage of banana (Musa AAA) and plantain (Musa AAB) peels on neutral detergent fibre, acid detergent fibre, cellulose, hemicelluloses, lignin, pectin contents, and pectin chemical features were studied. Plantain peels contained a higher amount of lignin but had a lower hemicellulose content than banana peels. A sequential extraction of pectins showed that acid extraction was the most efficient to isolate banana peel pectins, whereas an ammonium oxalate extraction was more appropriate for plantain peels. In all the stages of maturation, the pectin content in banana peels was higher compared to plantain peels. Moreover, the galacturonic acid and methoxy group contents in banana peels were higher than in plantain peels. The average molecular weights of the extracted pectins were in the range of 132.6-573.8 kDa and were not dependant on peel variety, while the stage of maturation did not affect the dietary fibre yields and the composition in pectic polysaccharides in a consistent manner. This study has showed that banana peels are a potential source of dietary fibres and pectins. PMID:17931857

  11. Dietary fibre components and pectin chemical features of peels during ripening in banana and plantain varieties.

    PubMed

    Happi Emaga, Thomas; Robert, Christelle; Ronkart, Sébastien N; Wathelet, Bernard; Paquot, Michel

    2008-07-01

    The effects of the ripeness stage of banana (Musa AAA) and plantain (Musa AAB) peels on neutral detergent fibre, acid detergent fibre, cellulose, hemicelluloses, lignin, pectin contents, and pectin chemical features were studied. Plantain peels contained a higher amount of lignin but had a lower hemicellulose content than banana peels. A sequential extraction of pectins showed that acid extraction was the most efficient to isolate banana peel pectins, whereas an ammonium oxalate extraction was more appropriate for plantain peels. In all the stages of maturation, the pectin content in banana peels was higher compared to plantain peels. Moreover, the galacturonic acid and methoxy group contents in banana peels were higher than in plantain peels. The average molecular weights of the extracted pectins were in the range of 132.6-573.8 kDa and were not dependant on peel variety, while the stage of maturation did not affect the dietary fibre yields and the composition in pectic polysaccharides in a consistent manner. This study has showed that banana peels are a potential source of dietary fibres and pectins.

  12. Wavelet approximation of correlated wave functions. II. Hyperbolic wavelets and adaptive approximation schemes

    NASA Astrophysics Data System (ADS)

    Luo, Hongjun; Kolb, Dietmar; Flad, Heinz-Jurgen; Hackbusch, Wolfgang; Koprucki, Thomas

    2002-08-01

    We have studied various aspects concerning the use of hyperbolic wavelets and adaptive approximation schemes for wavelet expansions of correlated wave functions. In order to analyze the consequences of reduced regularity of the wave function at the electron-electron cusp, we first considered a realistic exactly solvable many-particle model in one dimension. Convergence rates of wavelet expansions, with respect to L2 and H1 norms and the energy, were established for this model. We compare the performance of hyperbolic wavelets and their extensions through adaptive refinement in the cusp region, to a fully adaptive treatment based on the energy contribution of individual wavelets. Although hyperbolic wavelets show an inferior convergence behavior, they can be easily refined in the cusp region yielding an optimal convergence rate for the energy. Preliminary results for the helium atom are presented, which demonstrate the transferability of our observations to more realistic systems. We propose a contraction scheme for wavelets in the cusp region, which reduces the number of degrees of freedom and yields a favorable cost to benefit ratio for the evaluation of matrix elements.

  13. [Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].

    PubMed

    Zhang, Meiyun; Zhang, Benshu; Chen, Ying

    2014-08-01

    Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.

  14. Ultrasonic extraction of steroidal alkaloids from potato peel waste.

    PubMed

    Hossain, Mohammad B; Tiwari, Brijesh K; Gangopadhyay, Nirupama; O'Donnell, Colm P; Brunton, Nigel P; Rai, Dilip K

    2014-07-01

    Potato processors produce large volumes of waste in the form of potato peel which is either discarded or sold at a low price. Potato peel waste is a potential source of steroidal alkaloids which are biologically active secondary metabolites which could serve as precursors to agents with apoptotic, chemopreventive and anti-inflammatory properties. The present study investigated the relative efficacy of ultrasound assisted extraction (UAE) and solid liquid extraction (SLE) both using methanol, to extract steroidal alkaloids from potato peel waste and identified optimal conditions for UAE of α-solanine, α-chaconine, solanidine and demissidine. Using response surface methodology optimal UAE conditions were identified as an amplitude of 61 μm and an extraction time of 17 min which resulted the recovery of 1102 μg steroidal alkaloids/g dried potato peel (DPP). In contrast, SLE yielded 710.51 glycoalkaloid μg/g DPP. Recoveries of individual glycoalkoids using UAE yielded 273, 542.7, 231 and 55.3 μg/g DPP for α-solanine, α-chaconine, solanidine and demissidine respectively. Whereas for SLE yields were 180.3, 337.6, 160.2 and 32.4 μg/g DPP for α-solanine, α-chaconine, solanidine and demissidine respectively. The predicted values from the developed second order quadratic polynomial equation were in close agreement with the experimental values with low average mean deviation (E<5%) values. Predicted models were highly significant (p<0.05) for all parameters studied. This study indicates that UAE has strong potential as an extraction method for steroidal alkaloids from potato peel waste.

  15. Wavelet Analysis of Space Solar Telescope Images

    NASA Astrophysics Data System (ADS)

    Zhu, Xi-An; Jin, Sheng-Zhen; Wang, Jing-Yu; Ning, Shu-Nian

    2003-12-01

    The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

  16. Wavelet analysis for wind fields estimation.

    PubMed

    Leite, Gladeston C; Ushizima, Daniela M; Medeiros, Fátima N S; de Lima, Gilson G

    2010-01-01

    Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B(3) spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms(-1). Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms.

  17. Component identification of nonstationary signals using wavelets

    SciTech Connect

    Otaduy, P.J.; Georgevich, V. )

    1993-01-01

    Fourier analysis is based on the decomposition of a signal into a linear combination of integral dilations of the base function e[sup ix],i.e., of sinusoidal waves. The larger the dilation the higher the frequency of the sinusoidal component. Each frequency component is of constant magnitude along the signal length. Localized features are averaged over the signal's length; thus, time localization is absent. Wavelet analysis is based on the decomposition of a signal into a linear combination of binary dilations and dyadic translations of a base function with compact support, i.e., a basic wavelet. A basic wavelet function can be, with basic restrictions, any function suitable to be a window in both the time.

  18. Wavelet Analysis for Wind Fields Estimation

    PubMed Central

    Leite, Gladeston C.; Ushizima, Daniela M.; Medeiros, Fátima N. S.; de Lima, Gilson G.

    2010-01-01

    Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms. PMID:22219699

  19. Wavelet analysis for wind fields estimation.

    PubMed

    Leite, Gladeston C; Ushizima, Daniela M; Medeiros, Fátima N S; de Lima, Gilson G

    2010-01-01

    Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B(3) spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms(-1). Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms. PMID:22219699

  20. Characterization and simulation of gunfire with wavelets

    SciTech Connect

    Smallwood, D.O.

    1998-09-01

    Gunfire is used as an example to show how the wavelet transform can be used to characterize and simulate nonstationary random events when an ensemble of events is available. The response of a structure to nearby firing of a high-firing rate gun has been characterized in several ways as a nonstationary random process. The methods all used some form of the discrete fourier transform. The current paper will explore a simpler method to describe the nonstationary random process in terms of a wavelet transform. As was done previously, the gunfire record is broken up into a sequence of transient waveforms each representing the response to the firing of a single round. The wavelet transform is performed on each of these records. The mean and standard deviation of the resulting wavelet coefficients describe the composite characteristics of the entire waveform. It is shown that the distribution of the wavelet coefficients is approximately Gaussian with a nonzero mean and that the standard deviation of the coefficients at different times and levels are approximately independent. The gunfire is simulated by generating realizations of records of a single-round firing by computing the inverse wavelet transform from Gaussian random coefficients with the same mean and standard deviation as those estimated from the previously discussed gunfire record. The individual realizations are then assembled into a realization of a time history of many rounds firing. A second-order correction of the probability density function (pdf) is accomplished with a zero memory nonlinear (ZMNL) function. The method is straightforward, easy to implement, and produces a simulated record very much like the original measured gunfire record.

  1. [A novel denoising approach to SVD filtering based on DCT and PCA in CT image].

    PubMed

    Feng, Fuqiang; Wang, Jun

    2013-10-01

    Because of various effects of the imaging mechanism, noises are inevitably introduced in medical CT imaging process. Noises in the images will greatly degrade the quality of images and bring difficulties to clinical diagnosis. This paper presents a new method to improve singular value decomposition (SVD) filtering performance in CT image. Filter based on SVD can effectively analyze characteristics of the image in horizontal (and/or vertical) directions. According to the features of CT image, we can make use of discrete cosine transform (DCT) to extract the region of interest and to shield uninterested region so as to realize the extraction of structure characteristics of the image. Then we transformed SVD to the image after DCT, constructing weighting function for image reconstruction adaptively weighted. The algorithm for the novel denoising approach in this paper was applied in CT image denoising, and the experimental results showed that the new method could effectively improve the performance of SVD filtering.

  2. Multiresolution parametric estimation of transparent motions and denoising of fluoroscopic images.

    PubMed

    Auvray, Vincent; Liénard, Jean; Bouthemy, Patrick

    2005-01-01

    We describe a novel multiresolution parametric framework to estimate transparent motions typically present in X-Ray exams. Assuming the presence if two transparent layers, it computes two affine velocity fields by minimizing an appropriate objective function with an incremental Gauss-Newton technique. We have designed a realistic simulation scheme of fluoroscopic image sequences to validate our method on data with ground truth and different levels of noise. An experiment on real clinical images is also reported. We then exploit this transparent-motion estimation method to denoise two layers image sequences using a motion-compensated estimation method. In accordance with theory, we show that we reach a denoising factor of 2/3 in a few iterations without bringing any local artifacts in the image sequence.

  3. Projection domain denoising method based on dictionary learning for low-dose CT image reconstruction.

    PubMed

    Zhang, Haiyan; Zhang, Liyi; Sun, Yunshan; Zhang, Jingyu

    2015-01-01

    Reducing X-ray tube current is one of the widely used methods for decreasing the radiation dose. Unfortunately, the signal-to-noise ratio (SNR) of the projection data degrades simultaneously. To improve the quality of reconstructed images, a dictionary learning based penalized weighted least-squares (PWLS) approach is proposed for sinogram denoising. The weighted least-squares considers the statistical characteristic of noise and the penalty models the sparsity of sinogram based on dictionary learning. Then reconstruct CT image using filtered back projection (FBP) algorithm from the denoised sinogram. The proposed method is particularly suitable for the projection data with low SNR. Experimental results show that the proposed method can get high-quality CT images when the signal to noise ratio of projection data declines sharply.

  4. Analysis of wavelet technology for NASA applications

    NASA Technical Reports Server (NTRS)

    Wells, R. O., Jr.

    1994-01-01

    The purpose of this grant was to introduce a broad group of NASA researchers and administrators to wavelet technology and to determine its future role in research and development at NASA JSC. The activities of several briefings held between NASA JSC scientists and Rice University researchers are discussed. An attached paper, 'Recent Advances in Wavelet Technology', summarizes some aspects of these briefings. Two proposals submitted to NASA reflect the primary areas of common interest. They are image analysis and numerical solutions of partial differential equations arising in computational fluid dynamics and structural mechanics.

  5. Numerical Algorithms Based on Biorthogonal Wavelets

    NASA Technical Reports Server (NTRS)

    Ponenti, Pj.; Liandrat, J.

    1996-01-01

    Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.

  6. Wavelet analysis applied to the IRAS cirrus

    NASA Technical Reports Server (NTRS)

    Langer, William D.; Wilson, Robert W.; Anderson, Charles H.

    1994-01-01

    The structure of infrared cirrus clouds is analyzed with Laplacian pyramid transforms, a form of non-orthogonal wavelets. Pyramid and wavelet transforms provide a means to decompose images into their spatial frequency components such that all spatial scales are treated in an equivalent manner. The multiscale transform analysis is applied to IRAS 100 micrometer maps of cirrus emission in the north Galactic pole region to extract features on different scales. In the maps we identify filaments, fragments and clumps by separating all connected regions. These structures are analyzed with respect to their Hausdorff dimension for evidence of the scaling relationships in the cirrus clouds.

  7. Feasibility study of dose reduction in digital breast tomosynthesis using non-local denoising algorithms

    NASA Astrophysics Data System (ADS)

    Vieira, Marcelo A. C.; de Oliveira, Helder C. R.; Nunes, Polyana F.; Borges, Lucas R.; Bakic, Predrag R.; Barufaldi, Bruno; Acciavatti, Raymond J.; Maidment, Andrew D. A.

    2015-03-01

    The main purpose of this work is to study the ability of denoising algorithms to reduce the radiation dose in Digital Breast Tomosynthesis (DBT) examinations. Clinical use of DBT is normally performed in "combo-mode", in which, in addition to DBT projections, a 2D mammogram is taken with the standard radiation dose. As a result, patients have been exposed to radiation doses higher than used in digital mammography. Thus, efforts to reduce the radiation dose in DBT examinations are of great interest. However, a decrease in dose leads to an increased quantum noise level, and related decrease in image quality. This work is aimed at addressing this problem by the use of denoising techniques, which could allow for dose reduction while keeping the image quality acceptable. We have studied two "state of the art" denoising techniques for filtering the quantum noise due to the reduced dose in DBT projections: Non-local Means (NLM) and Block-matching 3D (BM3D). We acquired DBT projections at different dose levels of an anthropomorphic physical breast phantom with inserted simulated microcalcifications. Then, we found the optimal filtering parameters where the denoising algorithms are capable of recovering the quality from the DBT images acquired with the standard radiation dose. Results using objective image quality assessment metrics showed that BM3D algorithm achieved better noise adjustment (mean difference in peak signal to noise ratio < 0.1dB) and less blurring (mean difference in image sharpness ~ 6%) than the NLM for the projections acquired with lower radiation doses.

  8. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

    NASA Astrophysics Data System (ADS)

    Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

    2012-12-01

    Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that

  9. Wavelet-based detection of transients in biological signals

    NASA Astrophysics Data System (ADS)

    Mzaik, Tahsin; Jagadeesh, Jogikal M.

    1994-10-01

    This paper presents two multiresolution algorithms for detection and separation of mixed signals using the wavelet transform. The first algorithm allows one to design a mother wavelet and its associated wavelet grid that guarantees the separation of signal components if information about the expected minimum signal time and frequency separation of the individual components is known. The second algorithm expands this idea to design two mother wavelets which are then combined to achieve the required separation otherwise impossible with a single wavelet. Potential applications include many biological signals such as ECG, EKG, and retinal signals.

  10. EEG analysis using wavelet-based information tools.

    PubMed

    Rosso, O A; Martin, M T; Figliola, A; Keller, K; Plastino, A

    2006-06-15

    Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity.

  11. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal

    PubMed Central

    Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

    2015-01-01

    An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. PMID:26512665

  12. Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

    NASA Astrophysics Data System (ADS)

    Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

    2016-01-01

    In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

  13. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

    PubMed

    Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

    2015-10-23

    An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

  14. A New Image Denoising Algorithm that Preserves Structures of Astronomical Data

    NASA Astrophysics Data System (ADS)

    Bressert, Eli; Edmonds, P.; Kowal Arcand, K.

    2007-05-01

    We have processed numerous x-ray data sets using several well-known algorithms such as Gaussian and adaptive smoothing for public related image releases. These algorithms are used to denoise/smooth images and retain the overall structure of observed objects. Recently, a new PDE algorithm and program, provided by Dr. David Tschumperle and referred to as GREYCstoration, has been tested and is in the progress of being implemented into the Chandra EPO imaging group. Results of GREYCstoration will be presented and compared to the currently used methods for x-ray and multiple wavelength images. What demarcates Tschumperle's algorithm from the current algorithms used by the EPO imaging group is its ability to preserve the main structures of an image strongly, while reducing noise. In addition to denoising images, GREYCstoration can be used to erase artifacts accumulated during observation and mosaicing stages. GREYCstoration produces results that are comparable and in some cases more preferable than the current denoising/smoothing algorithms. From our early stages of testing, the results of the new algorithm will provide insight on the algorithm's initial capabilities on multiple wavelength astronomy data sets.

  15. Adaptive non-local means filtering based on local noise level for CT denoising

    NASA Astrophysics Data System (ADS)

    Li, Zhoubo; Yu, Lifeng; Trzasko, Joshua D.; Fletcher, Joel G.; McCollough, Cynthia H.; Manduca, Armando

    2012-03-01

    Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.

  16. Wavelet based automated postural event detection and activity classification with single imu - biomed 2013.

    PubMed

    Lockhart, Thurmon E; Soangra, Rahul; Zhang, Jian; Wu, Xuefan

    2013-01-01

    and classification algorithm using denoised signals from single wireless IMU placed at sternum. The algorithm was further validated and verified with motion capture system in laboratory environment. Wavelet denoising highlighted postural events and transition durations that further provided clinical information on postural control and motor coordination. The presented method can be applied in real life ambulatory monitoring approaches for assessing condition of elderly. PMID:23686204

  17. Diagnostic accuracy of late iodine enhancement on cardiac computed tomography with a denoise filter for the evaluation of myocardial infarction.

    PubMed

    Matsuda, Takuya; Kido, Teruhito; Itoh, Toshihide; Saeki, Hideyuki; Shigemi, Susumu; Watanabe, Kouki; Kido, Tomoyuki; Aono, Shoji; Yamamoto, Masaya; Matsuda, Takeshi; Mochizuki, Teruhito

    2015-12-01

    We evaluated the image quality and diagnostic performance of late iodine enhancement (LIE) in dual-source computed tomography (DSCT) with low kilo-voltage peak (kVp) images and a denoise filter for the detection of acute myocardial infarction (AMI) in comparison with late gadolinium enhancement (LGE) magnetic resonance imaging (MRI). The Hospital Ethics Committee approved the study protocol. Before discharge, 19 patients who received percutaneous coronary intervention after AMI underwent DSCT and 1.5 T MRI. Immediately after coronary computed tomography (CT) angiography, contrast medium was administered at a slow injection rate. LIE-CT scans were acquired via dual-energy CT and reconstructed as 100-, 140-kVp, and mixed images. An iterative three-dimensional edge-preserved smoothing filter was applied to the 100-kVp images to obtain denoised 100-kVp images. The mixed, 140-kVp, 100-kVp, and denoised 100-kVp images were assessed using contrast-to-noise ratio (CNR), and their diagnostic performance in comparison with MRI and infarcted volumes were evaluated. Three hundred four segments of 19 patients were evaluated. Fifty-three segments showed LGE in MRI. The median CNR of the mixed, 140-, 100-kVp and denoised 100-kVp images was 3.49, 1.21, 3.57, and 6.08, respectively. The median CNR was significantly higher in the denoised 100-kVp images than in the other three images (P < 0.05). The denoised 100-kVp images showed the highest diagnostic accuracy and sensitivity. The percentage of myocardium in the four CT image types was significantly correlated with the respective MRI findings. The use of a denoise filter with a low-kVp image can improve CNR, sensitivity, and accuracy in LIE-CT.

  18. Diagnostic accuracy of late iodine enhancement on cardiac computed tomography with a denoise filter for the evaluation of myocardial infarction.

    PubMed

    Matsuda, Takuya; Kido, Teruhito; Itoh, Toshihide; Saeki, Hideyuki; Shigemi, Susumu; Watanabe, Kouki; Kido, Tomoyuki; Aono, Shoji; Yamamoto, Masaya; Matsuda, Takeshi; Mochizuki, Teruhito

    2015-12-01

    We evaluated the image quality and diagnostic performance of late iodine enhancement (LIE) in dual-source computed tomography (DSCT) with low kilo-voltage peak (kVp) images and a denoise filter for the detection of acute myocardial infarction (AMI) in comparison with late gadolinium enhancement (LGE) magnetic resonance imaging (MRI). The Hospital Ethics Committee approved the study protocol. Before discharge, 19 patients who received percutaneous coronary intervention after AMI underwent DSCT and 1.5 T MRI. Immediately after coronary computed tomography (CT) angiography, contrast medium was administered at a slow injection rate. LIE-CT scans were acquired via dual-energy CT and reconstructed as 100-, 140-kVp, and mixed images. An iterative three-dimensional edge-preserved smoothing filter was applied to the 100-kVp images to obtain denoised 100-kVp images. The mixed, 140-kVp, 100-kVp, and denoised 100-kVp images were assessed using contrast-to-noise ratio (CNR), and their diagnostic performance in comparison with MRI and infarcted volumes were evaluated. Three hundred four segments of 19 patients were evaluated. Fifty-three segments showed LGE in MRI. The median CNR of the mixed, 140-, 100-kVp and denoised 100-kVp images was 3.49, 1.21, 3.57, and 6.08, respectively. The median CNR was significantly higher in the denoised 100-kVp images than in the other three images (P < 0.05). The denoised 100-kVp images showed the highest diagnostic accuracy and sensitivity. The percentage of myocardium in the four CT image types was significantly correlated with the respective MRI findings. The use of a denoise filter with a low-kVp image can improve CNR, sensitivity, and accuracy in LIE-CT. PMID:26202159

  19. Improvement of Biogas Production from Orange Peel Waste by Leaching of Limonene

    PubMed Central

    Wikandari, Rachma; Nguyen, Huong; Millati, Ria; Niklasson, Claes; Taherzadeh, Mohammad J.

    2015-01-01

    Limonene is present in orange peel wastes and is known as an antimicrobial agent, which impedes biogas production when digesting the peels. In this work, pretreatment of the peels to remove limonene under mild condition was proposed by leaching of limonene using hexane as solvent. The pretreatments were carried out with homogenized or chopped orange peel at 20–40°C with orange peel waste and hexane ratio (w/v) ranging from 1 : 2 to 1 : 12 for 10 to 300 min. The pretreated peels were then digested in batch reactors for 33 days. The highest biogas production was achieved by treating chopped orange peel waste and hexane ratio of 12 : 1 at 20°C for 10 min corresponding to more than threefold increase of biogas production from 0.061 to 0.217 m3 methane/kg VS. The solvent recovery was 90% using vacuum filtration and needs further separation using evaporation. The hexane residue in the peel had a negative impact on biogas production as shown by 28.6% reduction of methane and lower methane production of pretreated orange peel waste in semicontinuous digestion system compared to that of untreated peel. PMID:25866787

  20. Improvement of biogas production from orange peel waste by leaching of limonene.

    PubMed

    Wikandari, Rachma; Nguyen, Huong; Millati, Ria; Niklasson, Claes; Taherzadeh, Mohammad J

    2015-01-01

    Limonene is present in orange peel wastes and is known as an antimicrobial agent, which impedes biogas production when digesting the peels. In this work, pretreatment of the peels to remove limonene under mild condition was proposed by leaching of limonene using hexane as solvent. The pretreatments were carried out with homogenized or chopped orange peel at 20-40°C with orange peel waste and hexane ratio (w/v) ranging from 1 : 2 to 1 : 12 for 10 to 300 min. The pretreated peels were then digested in batch reactors for 33 days. The highest biogas production was achieved by treating chopped orange peel waste and hexane ratio of 12 : 1 at 20°C for 10 min corresponding to more than threefold increase of biogas production from 0.061 to 0.217 m(3) methane/kg VS. The solvent recovery was 90% using vacuum filtration and needs further separation using evaporation. The hexane residue in the peel had a negative impact on biogas production as shown by 28.6% reduction of methane and lower methane production of pretreated orange peel waste in semicontinuous digestion system compared to that of untreated peel.

  1. Improvement of biogas production from orange peel waste by leaching of limonene.

    PubMed

    Wikandari, Rachma; Nguyen, Huong; Millati, Ria; Niklasson, Claes; Taherzadeh, Mohammad J

    2015-01-01

    Limonene is present in orange peel wastes and is known as an antimicrobial agent, which impedes biogas production when digesting the peels. In this work, pretreatment of the peels to remove limonene under mild condition was proposed by leaching of limonene using hexane as solvent. The pretreatments were carried out with homogenized or chopped orange peel at 20-40°C with orange peel waste and hexane ratio (w/v) ranging from 1 : 2 to 1 : 12 for 10 to 300 min. The pretreated peels were then digested in batch reactors for 33 days. The highest biogas production was achieved by treating chopped orange peel waste and hexane ratio of 12 : 1 at 20°C for 10 min corresponding to more than threefold increase of biogas production from 0.061 to 0.217 m(3) methane/kg VS. The solvent recovery was 90% using vacuum filtration and needs further separation using evaporation. The hexane residue in the peel had a negative impact on biogas production as shown by 28.6% reduction of methane and lower methane production of pretreated orange peel waste in semicontinuous digestion system compared to that of untreated peel. PMID:25866787

  2. A comparison of dynamic mechanical properties of processing-tomato peel as affected by hot lye and infrared radiation heating for peeling

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study investigated the viscoelastic characteristics of tomato skins subjected to conventional hot lye peeling and emerging infrared-dry peeling by using dynamic mechanical analysis (DMA). Three DMA testing modes, including temperature ramp, frequency sweep, and creep behavior test, were conduct...

  3. Understanding wavelet analysis and filters for engineering applications

    NASA Astrophysics Data System (ADS)

    Parameswariah, Chethan Bangalore

    Wavelets are signal-processing tools that have been of interest due to their characteristics and properties. Clear understanding of wavelets and their properties are a key to successful applications. Many theoretical and application-oriented papers have been written. Yet the choice of a right wavelet for a given application is an ongoing quest that has not been satisfactorily answered. This research has successfully identified certain issues, and an effort has been made to provide an understanding of wavelets by studying the wavelet filters in terms of their pole-zero and magnitude-phase characteristics. The magnitude characteristics of these filters have flat responses in both the pass band and stop band. The phase characteristics are almost linear. It is interesting to observe that some wavelets have the exact same magnitude characteristics but their phase responses vary in the linear slopes. An application of wavelets for fast detection of the fault current in a transformer and distinguishing from the inrush current clearly shows the advantages of the lower slope and fewer coefficients---Daubechies wavelet D4 over D20. This research has been published in the IEEE transactions on Power systems and is also proposed as an innovative method for protective relaying techniques. For detecting the frequency composition of the signal being analyzed, an understanding of the energy distribution in the output wavelet decompositions is presented for different wavelet families. The wavelets with fewer coefficients in their filters have more energy leakage into adjacent bands. The frequency bandwidth characteristics display flatness in the middle of the pass band confirming that the frequency of interest should be in the middle of the frequency band when performing a wavelet transform. Symlets exhibit good flatness with minimum ripple but the transition regions do not have sharper cut off. The number of wavelet levels and their frequency ranges are dependent on the two

  4. Information retrieval system utilizing wavelet transform

    DOEpatents

    Brewster, Mary E.; Miller, Nancy E.

    2000-01-01

    A method for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.

  5. Nonlinear adaptive wavelet analysis of electrocardiogram signals

    NASA Astrophysics Data System (ADS)

    Yang, H.; Bukkapatnam, S. T.; Komanduri, R.

    2007-08-01

    Wavelet representation can provide an effective time-frequency analysis for nonstationary signals, such as the electrocardiogram (EKG) signals, which contain both steady and transient parts. In recent years, wavelet representation has been emerging as a powerful time-frequency tool for the analysis and measurement of EKG signals. The EKG signals contain recurring, near-periodic patterns of P , QRS , T , and U waveforms, each of which can have multiple manifestations. Identification and extraction of a compact set of features from these patterns is critical for effective detection and diagnosis of various disorders. This paper presents an approach to extract a fiducial pattern of EKG based on the consideration of the underlying nonlinear dynamics. The pattern, in a nutshell, is a combination of eigenfunctions of the ensembles created from a Poincare section of EKG dynamics. The adaptation of wavelet functions to the fiducial pattern thus extracted yields two orders of magnitude (some 95%) more compact representation (measured in terms of Shannon signal entropy). Such a compact representation can facilitate in the extraction of features that are less sensitive to extraneous noise and other variations. The adaptive wavelet can also lead to more efficient algorithms for beat detection and QRS cancellation as well as for the extraction of multiple classical EKG signal events, such as widths of QRS complexes and QT intervals.

  6. Parallel adaptive wavelet collocation method for PDEs

    SciTech Connect

    Nejadmalayeri, Alireza; Vezolainen, Alexei; Brown-Dymkoski, Eric; Vasilyev, Oleg V.

    2015-10-01

    A parallel adaptive wavelet collocation method for solving a large class of Partial Differential Equations is presented. The parallelization is achieved by developing an asynchronous parallel wavelet transform, which allows one to perform parallel wavelet transform and derivative calculations with only one data synchronization at the highest level of resolution. The data are stored using tree-like structure with tree roots starting at a priori defined level of resolution. Both static and dynamic domain partitioning approaches are developed. For the dynamic domain partitioning, trees are considered to be the minimum quanta of data to be migrated between the processes. This allows fully automated and efficient handling of non-simply connected partitioning of a computational domain. Dynamic load balancing is achieved via domain repartitioning during the grid adaptation step and reassigning trees to the appropriate processes to ensure approximately the same number of grid points on each process. The parallel efficiency of the approach is discussed based on parallel adaptive wavelet-based Coherent Vortex Simulations of homogeneous turbulence with linear forcing at effective non-adaptive resolutions up to 2048{sup 3} using as many as 2048 CPU cores.

  7. Characterization and Simulation of Gunfire with Wavelets

    DOE PAGES

    Smallwood, David O.

    1999-01-01

    Gunfire is used as an example to show how the wavelet transform can be used to characterize and simulate nonstationary random events when an ensemble of events is available. The structural response to nearby firing of a high-firing rate gun has been characterized in several ways as a nonstationary random process. The current paper will explore a method to describe the nonstationary random process using a wavelet transform. The gunfire record is broken up into a sequence of transient waveforms each representing the response to the firing of a single round. A wavelet transform is performed on each of thesemore » records. The gunfire is simulated by generating realizations of records of a single-round firing by computing an inverse wavelet transform from Gaussian random coefficients with the same mean and standard deviation as those estimated from the previously analyzed gunfire record. The individual records are assembled into a realization of many rounds firing. A second-order correction of the probability density function is accomplished with a zero memory nonlinear function. The method is straightforward, easy to implement, and produces a simulated record much like the measured gunfire record.« less

  8. Cosmic Ray elimination using the Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Orozco-Aguilera, M. T.; Cruz, J.; Altamirano, L.; Serrano, A.

    2009-11-01

    In this work, we present a method for the automatic cosmic ray elimination in a single CCD exposure using the Wavelet Transform. The proposed method can eliminate cosmic rays of any shape or size. With this method we can eliminate over 95% of cosmic rays in a spectral image.

  9. Climate wavelet spectrum estimation under chronology uncertainties

    NASA Astrophysics Data System (ADS)

    Lenoir, G.; Crucifix, M.

    2012-04-01

    Several approaches to estimate the chronology of palaeoclimate records exist in the literature: simple interpolation between the tie points, orbital tuning, alignment on other data... These techniques generate a single estimate of the chronology. More recently, statistical generators of chronologies have appeared (e.g. OXCAL, BCHRON) allowing the construction of thousands of chronologies given the tie points and their uncertainties. These techniques are based on advanced statistical methods. They allow one to take into account the uncertainty of the timing of each climatic event recorded into the core. On the other hand, when interpreting the data, scientists often rely on time series analysis, and especially on spectral analysis. Given that paleo-data are composed of a large spectrum of frequencies, are non-stationary and are highly noisy, the continuous wavelet transform turns out to be a suitable tool to analyse them. The wavelet periodogram, in particular, is helpful to interpret visually the time-frequency behaviour of the data. Here, we combine statistical methods to generate chronologies with the power of continuous wavelet transform. Some interesting applications then come up: comparison of time-frequency patterns between two proxies (extracted from different cores), between a proxy and a statistical dynamical model, and statistical estimation of phase-lag between two filtered signals. All these applications consider explicitly the uncertainty in the chronology. The poster presents mathematical developments on the wavelet spectrum estimation under chronology uncertainties as well as some applications to Quaternary data based on marine and ice cores.

  10. Biocomposites reinforced with cellulose nanocrystals derived from potato peel waste.

    PubMed

    Chen, D; Lawton, D; Thompson, M R; Liu, Q

    2012-09-01

    This study investigated the effectiveness of cellulose nanocrystals derived from potato peel waste as a reinforcement and vapor barrier additive. The nanocrystals were derived from cellulosic material in the potato peel by alkali treatment and subsequently acid hydrolysis. TEM images revealed the average fiber length of the nanocrystals was 410 nm with an aspect ratio of 41; its aspect ratio being considerably larger than cotton-derived nanocrystals prepared using similar reaction conditions. Cellulose nanocrystals (CNC)-filled polyvinyl alcohol (PVA) and thermoplastic starch (TPS) films were prepared by solution casting method to maintain uniform dispersion of the 1-2% (w/w) filler content. An increase of 19% and 33% (starch composite) and 38% and 49% (PVA composite) in tensile modulus was observed for the 1% and 2% CNC-reinforced composites, respectively. Water vapor transmission measurements showed a marginal reduction of water permeability for the PVA composite, whereas no effect was observed for the thermoplastic starch composite.

  11. Laserpeel: a peeling concept revolution with laser resurfacing protocols

    NASA Astrophysics Data System (ADS)

    Tenenbaum, Alain

    2000-06-01

    The author who is inventor of EasyPeel then Laserpeel wants to introduce new ways to choose the right indications for patients asking for cosmetic surgery. A lifting is as if you take a shirt and want to reduce its size cutting it. A resurfacing is as if you put a shirt and want to iron it. A peeling was as if you changed the color and grain of the shirt. Laserpeel is as if you iron the shirt treated with amidon, transform the second hand shirt as new, up to date on with glance effect sand give it then a stretching disco new wave effect. So, indications of facial lifting decrease at the same speed at the increase of indications of 'LASERPEEL'. Laser CO2 resurfacing should reborn because the post redness appearance decreases in intensity and duration due to LASERPEEL. LASERPEEL should be considered too as a preventive therapy coupled with preventive treatment resulting from longevity tests.

  12. Antioxidant properties of peel and pulp hydro extract in ten Persian pomegranate cultivars.

    PubMed

    Hajimahmoodi, M; Oveisi, M R; Sadeghi, N; Jannat, B; Hadjibabaie, M; Farahani, E; Akrami, M R; Namdar, R

    2008-06-15

    This study compares the antioxidant activity of ten different pomegranate cultivars grown in Iran using the ferric reducing power assay (FRAP assay), which is based on the reduction of a ferric-tripyridyl triazine complex to its ferrous, colored form in the presence of antioxidants. Aqueous solutions of known Fe(+2) concentration, in the range of 100-1000 micromol L(-1) were used for calibration. The results showed that among pulp and peel fractions the sour alac and sweet white peel cultivars had more FRAP value respectively. The pomegranate peel extract had markedly higher antioxidant capacity than the pulp extract. The peel extract of sweet white peel cultivar appeared to have more potential as a health supplement rich in natural antioxidants compared to the pulp and peel extracts of other pomegranate cultivars. PMID:18819648

  13. Microwave properties of peeled HEMT devices sapphire substrates

    NASA Technical Reports Server (NTRS)

    Young, Paul G.; Alterovitz, Samuel A.; Mena, Rafael A.; Smith, Edwyn D.

    1992-01-01

    The focus of this research is to demonstrate the first full radio frequency characterization of high electron mobility transistor (HEMT) device parameters. The results of this research are used in the design of circuits with peeled HEMT devices, e.g. 10 GHz amplifiers. Devices were fabricated using two HEMT structures grown by molecular beam epitaxy methods. A 500 A AlAs release layer for 'peel off' was included under the active layers of the structure. The structures are a homogeneously doped Al(0.3)GA(0.7)As/GaAs and a delta doped square well Al(.23)Ga(.77)As/GaAs HEMT structure. Devices were fabricated using a mesa isolation process. Contacts were done by sequentially evaporating Au/Ge/Au/Ni/Au followed by rapid thermal anneal at 400 C for 15 seconds. Gates were wet etch recessed and 1 to 1.4 micron Ti/Au gate metal was deposited. Devices were peeled off the GaAs substrate using Apiezon wax to support the active layer and a HF:DI (1:10) solution to remove the AlAs separation layer. Devices were then attached to sapphire substrates using van der Waals bonding.

  14. Bioflavour production from orange peel hydrolysate using immobilized Saccharomyces cerevisiae.

    PubMed

    Lalou, Sofia; Mantzouridou, Fani; Paraskevopoulou, Adamantini; Bugarski, Branko; Levic, Steva; Nedovic, Victor

    2013-11-01

    The rising trend of bioflavour synthesis by microorganisms is hindered by the high manufacturing costs, partially attributed to the cost of the starting material. To overcome this limitation, in the present study, dilute-acid hydrolysate of orange peel was employed as a low-cost, rich in fermentable sugars substrate for the production of flavour-active compounds by Saccharomyces cerevisiae. With this purpose, the use of immobilized cell technology to protect cells against the various inhibitory compounds present in the hydrolysate was evaluated with regard to yeast viability, carbon and nitrogen consumption and cell ability to produce flavour active compounds. For cell immobilization the encapsulation in Ca alginate beads was used. The results were compared with those obtained using free-cell system. Based on the data obtained immobilized cells showed better growth performance and increased ability for de novo synthesis of volatile esters of "fruity" aroma (phenylethyl acetate, ethyl hexanoate, octanoate, decanoate and dodecanoate) than those of free cells. The potential for in situ production of new formulations containing flavour-active compounds derive from yeast cells and also from essential oil of orange peel (limonene, α-terpineol) was demonstrated by the fact that bioflavour mixture was found to accumulate within the beads. Furthermore, the ability of the immobilized yeast to perform efficiently repeated batch fermentations of orange peel hydrolysate for bioflavour production was successfully maintained after six consecutive cycles of a total period of 240 h. PMID:23995224

  15. Cyanogenic potential of cassava peels and their detoxification for utilization as livestock feed.

    PubMed

    Tweyongyere, Robert; Katongole, Ignatious

    2002-12-01

    This study determined the cyanogenic potential of the cassava peels and assess the effectiveness of sun drying, heap fermentation and wet fermentation (soaking) in reducing the cyanide potential of the peels. Fresh cassava peels from major fresh food markets in Kampala and cassava grown in various parts of Uganda from Namolonge Agricultural and Animal Research Institute were used. The fresh peels from the market were subjected to the different detoxification methods foe 5 d; the cyanide potential was determined by enzymatic assay. The mean potential of the cassava peels from the food markets Kampala was 856 mg cyanide equivalen/kg of dry matter. The potential of the peels of the 14 cultivars fell between 253 and 1081 mg cyanide eQuivalent/kg of dry matter. High cyanogenic potential cultivars dominate on the market and pose danger of poisoning to livestock fed on fresh cassava peels. Treatment of the peels by sun-drying, heap fermentation on soaking reduced the cyanide potential to below 100 mg cyanide equivalent/kg of dry matter at 48, 72 and 96 h respectively. Sun-dying caused an early sharp fall in the cyanide potential, but heap fermentation or soaking gave the lowest residual cyanide after 120 h. Cassava peels could be safely used as livestock feed if they are treated to reduce the cyanogenic potential.

  16. Optics for produce quality evaluation: laser diffusion for orange peel thickness measurement

    NASA Astrophysics Data System (ADS)

    Affeldt, Henry A., Jr.; Heck, Richard D.

    1993-05-01

    A new sensing technique was investigated to nondestructively measure the peel thickness of oranges destined for fresh market consumption. Coherent polarized laser emissions diffused by the subcuticular layers of the peel were filtered and imaged into a matrix CCD camera. Images were analyzed using conventional high-speed pixel operations. Resulting correlations suggest that this method may be a successful tool in real-time food processing operations providing the packer and the consumer with an objective evaluation of peel thickness, and subsequently, edible volume, juice content, and the ease with which the peel can be removed.

  17. Feature selection using Haar wavelet power spectrum

    PubMed Central

    Subramani, Prabakaran; Sahu, Rajendra; Verma, Shekhar

    2006-01-01

    Background Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. Results Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. Conclusion In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for

  18. Performance evaluation and optimization of BM4D-AV denoising algorithm for cone-beam CT images

    NASA Astrophysics Data System (ADS)

    Huang, Kuidong; Tian, Xiaofei; Zhang, Dinghua; Zhang, Hua

    2015-12-01

    The broadening application of cone-beam Computed Tomography (CBCT) in medical diagnostics and nondestructive testing, necessitates advanced denoising algorithms for its 3D images. The block-matching and four dimensional filtering algorithm with adaptive variance (BM4D-AV) is applied to the 3D image denoising in this research. To optimize it, the key filtering parameters of the BM4D-AV algorithm are assessed firstly based on the simulated CBCT images and a table of optimized filtering parameters is obtained. Then, considering the complexity of the noise in realistic CBCT images, possible noise standard deviations in BM4D-AV are evaluated to attain the chosen principle for the realistic denoising. The results of corresponding experiments demonstrate that the BM4D-AV algorithm with optimized parameters presents excellent denosing effect on the realistic 3D CBCT images.

  19. A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising

    NASA Astrophysics Data System (ADS)

    Dawood Al-Dabbagh, Mohanad; Dawoud Al-Dabbagh, Rawaa; Raja Abdullah, R. S. A.; Hashim, F.

    2015-06-01

    The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isolated from noise distortion. The modified method showed significant improvements in performance over traditional de-noising techniques.

  20. Denoising techniques combined to Monte Carlo simulations for the prediction of high-resolution portal images in radiotherapy treatment verification

    NASA Astrophysics Data System (ADS)

    Lazaro, D.; Barat, E.; Le Loirec, C.; Dautremer, T.; Montagu, T.; Guérin, L.; Batalla, A.

    2013-05-01

    This work investigates the possibility of combining Monte Carlo (MC) simulations to a denoising algorithm for the accurate prediction of images acquired using amorphous silicon (a-Si) electronic portal imaging devices (EPIDs). An accurate MC model of the Siemens OptiVue1000 EPID was first developed using the penelope code, integrating a non-uniform backscatter modelling. Two already existing denoising algorithms were then applied on simulated portal images, namely the iterative reduction of noise (IRON) method and the locally adaptive Savitzky-Golay (LASG) method. A third denoising method, based on a nonparametric Bayesian framework and called DPGLM (for Dirichlet process generalized linear model) was also developed. Performances of the IRON, LASG and DPGLM methods, in terms of smoothing capabilities and computation time, were compared for portal images computed for different values of the RMS pixel noise (up to 10%) in three different configurations, a heterogeneous phantom irradiated by a non-conformal 15 × 15 cm2 field, a conformal beam from a pelvis treatment plan, and an IMRT beam from a prostate treatment plan. For all configurations, DPGLM outperforms both IRON and LASG by providing better smoothing performances and demonstrating a better robustness with respect to noise. Additionally, no parameter tuning is required by DPGLM, which makes the denoising step very generic and easy to handle for any portal image. Concerning the computation time, the denoising of 1024 × 1024 images takes about 1 h 30 min, 2 h and 5 min using DPGLM, IRON, and LASG, respectively. This paper shows the feasibility to predict within a few hours and with the same resolution as real images accurate portal images, combining MC simulations with the DPGLM denoising algorithm.

  1. Wavelet analysis and applications to some dynamical systems

    NASA Astrophysics Data System (ADS)

    Bendjoya, Ph.; Slezak, E.

    1993-05-01

    The main properties of the wavelet transform as a new time-frequency method which is particularly well suited for detecting and localizing discontinuities and scaling behavior in signals are reviewed. Particular attention is given to first applications of the wavelet transform to dynamical systems including solution of partial differential equations, fractal and turbulence characterization, and asteroid family determination from cluster analysis. Advantages of the wavelet transform over classical analysis methods are summarized.

  2. Embedded wavelet packet transform technique for texture compression

    NASA Astrophysics Data System (ADS)

    Li, Jin; Cheng, Po-Yuen; Kuo, C.-C. Jay

    1995-09-01

    A highly efficient texture compression scheme is proposed in this research. With this scheme, energy compaction of texture images is first achieved by the wavelet packet transform, and an embedding approach is then adopted for the coding of the wavelet packet transform coefficients. By comparing the proposed algorithm with the JPEG standard, FBI wavelet/scalar quantization standard and the EZW scheme with extensive experimental results, we observe a significant improvement in the rate-distortion performance and visual quality.

  3. Variability of Solar Irradiances Using Wavelet Analysis

    NASA Technical Reports Server (NTRS)

    Pesnell, William D.

    2007-01-01

    We have used wavelets to analyze the sunspot number, F10.7 (the solar irradiance at a wavelength of approx.10.7 cm), and Ap (a geomagnetic activity index). Three different wavelets are compared, showing how each selects either temporal or scale resolution. Our goal is an envelope of solar activity that better bounds the large amplitude fluctuations form solar minimum to maximum. We show how the 11-year cycle does not disappear at solar minimum, that minimum is only the other part of the solar cycle. Power in the fluctuations of solar-activity-related indices may peak during solar maximum but the solar cycle itself is always present. The Ap index has a peak after solar maximum that appears to be better correlated with the current solar cycle than with the following cycle.

  4. Wavelets for full reconfigurable ECG acquisition system

    NASA Astrophysics Data System (ADS)

    Morales, D. P.; García, A.; Castillo, E.; Meyer-Baese, U.; Palma, A. J.

    2011-06-01

    This paper presents the use of wavelet cores for a full reconfigurable electrocardiogram signal (ECG) acquisition system. The system is compound by two reconfigurable devices, a FPGA and a FPAA. The FPAA is in charge of the ECG signal acquisition, since this device is a versatile and reconfigurable analog front-end for biosignals. The FPGA is in charge of FPAA configuration, digital signal processing and information extraction such as heart beat rate and others. Wavelet analysis has become a powerful tool for ECG signal processing since it perfectly fits ECG signal shape. The use of these cores has been integrated in the LabVIEW FPGA module development tool that makes possible to employ VHDL cores within the usual LabVIEW graphical programming environment, thus freeing the designer from tedious and time consuming design of communication interfaces. This enables rapid test and graphical representation of results.

  5. Wavelet packet entropy for heart murmurs classification.

    PubMed

    Safara, Fatemeh; Doraisamy, Shyamala; Azman, Azreen; Jantan, Azrul; Ranga, Sri

    2012-01-01

    Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

  6. Wavelets and their applications past and future

    NASA Astrophysics Data System (ADS)

    Coifman, Ronald R.

    2009-04-01

    As this is a conference on mathematical tools for defense, I would like to dedicate this talk to the memory of Louis Auslander, who through his insights and visionary leadership, brought powerful new mathematics into DARPA, he has provided the main impetus to the development and insertion of wavelet based processing in defense. My goal here is to describe the evolution of a stream of ideas in Harmonic Analysis, ideas which in the past have been mostly applied for the analysis and extraction of information from physical data, and which now are increasingly applied to organize and extract information and knowledge from any set of digital documents, from text to music to questionnaires. This form of signal processing on digital data, is part of the future of wavelet analysis.

  7. Wavelet analysis of the impedance cardiogram waveforms

    NASA Astrophysics Data System (ADS)

    Podtaev, S.; Stepanov, R.; Dumler, A.; Chugainov, S.; Tziberkin, K.

    2012-12-01

    Impedance cardiography has been used for diagnosing atrial and ventricular dysfunctions, valve disorders, aortic stenosis, and vascular diseases. Almost all the applications of impedance cardiography require determination of some of the characteristic points of the ICG waveform. The ICG waveform has a set of characteristic points known as A, B, E ((dZ/dt)max) X, Y, O and Z. These points are related to distinct physiological events in the cardiac cycle. Objective of this work is an approbation of a new method of processing and interpretation of the impedance cardiogram waveforms using wavelet analysis. A method of computer thoracic tetrapolar polyrheocardiography is used for hemodynamic registrations. Use of original wavelet differentiation algorithm allows combining filtration and calculation of the derivatives of rheocardiogram. The proposed approach can be used in clinical practice for early diagnostics of cardiovascular system remodelling in the course of different pathologies.

  8. Propagating unstable wavelets in cardiac tissue

    NASA Astrophysics Data System (ADS)

    Boyle, Patrick M.; Madhavan, Adarsh; Reid, Matthew P.; Vigmond, Edward J.

    2012-01-01

    Solitonlike propagating modes have been proposed for excitable tissue, but have never been measured in cardiac tissue. In this study, we simulate an experimental protocol to elicit these propagating unstable wavelets (PUWs) in a detailed three-dimensional ventricular wedge preparation. PUWs appear as fixed-shape wavelets that propagate only in the direction of cardiac fibers, with conduction velocity approximately 40% slower than normal action potential excitation. We investigate their properties, demonstrating that PUWs are not true solitons. The range of stimuli for which PUWs were elicited was very narrow (several orders of magnitude lower than the stimulus strength itself), but increased with reduced sodium conductance and reduced coupling in nonlongitudinal directions. We show that the phenomenon does not depend on the particular membrane representation used or the shape of the stimulating electrode.

  9. Terrestrial Laser Scanner Data Denoising by Dictionary Learning of Sparse Coding

    NASA Astrophysics Data System (ADS)

    Smigiel, E.; Alby, E.; Grussenmeyer, P.

    2013-07-01

    Point cloud processing is basically a signal processing issue. The huge amount of data which are collected with Terrestrial Laser Scanners or photogrammetry techniques faces the classical questions linked with signal or image processing. Among others, denoising and compression are questions which have to be addressed in this context. That is why, one has to turn attention to signal theory because it is susceptible to guide one's good practices or to inspire new ideas from the latest developments of this field. The literature have been showing for decades how strong and dynamic, the theoretical field is and how efficient the derived algorithms have become. For about ten years, a new technique has appeared: known as compressive sensing or compressive sampling, it is based first on sparsity which is an interesting characteristic of many natural signals. Based on this concept, many denoising and compression techniques have shown their efficiencies. Sparsity can also be seen as redundancy removal of natural signals. Taken along with incoherent measurements, compressive sensing has appeared and uses the idea that redundancy could be removed at the very early stage of sampling. Hence, instead of sampling the signal at high sampling rate and removing redundancy as a second stage, the acquisition stage itself may be run with redundancy removal. This paper gives some theoretical aspects of these ideas with first simple mathematics. Then, the idea of compressive sensing for a Terrestrial Laser Scanner is examined as a potential research question and finally, a denoising scheme based on a dictionary learning of sparse coding is experienced. Both the theoretical discussion and the obtained results show that it is worth staying close to signal processing theory and its community to take benefit of its latest developments.

  10. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    PubMed

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  11. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    PubMed

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time. PMID:26405887

  12. Development of wavelet analysis tools for turbulence

    NASA Technical Reports Server (NTRS)

    Bertelrud, A.; Erlebacher, G.; Dussouillez, PH.; Liandrat, M. P.; Liandrat, J.; Bailly, F. Moret; Tchamitchian, PH.

    1992-01-01

    Presented here is the general framework and the initial results of a joint effort to derive novel research tools and easy to use software to analyze and model turbulence and transition. Given here is a brief review of the issues, a summary of some basic properties of wavelets, and preliminary results. Technical aspects of the implementation, the physical conclusions reached at this time, and current developments are discussed.

  13. Multiscale peak detection in wavelet space.

    PubMed

    Zhang, Zhi-Min; Tong, Xia; Peng, Ying; Ma, Pan; Zhang, Ming-Jin; Lu, Hong-Mei; Chen, Xiao-Qing; Liang, Yi-Zeng

    2015-12-01

    Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

  14. Wavelet features in motion data classification

    NASA Astrophysics Data System (ADS)

    Szczesna, Agnieszka; Świtoński, Adam; Słupik, Janusz; Josiński, Henryk; Wojciechowski, Konrad

    2016-06-01

    The paper deals with the problem of motion data classification based on result of multiresolution analysis implemented in form of quaternion lifting scheme. Scheme processes directly on time series of rotations coded in form of unit quaternion signal. In the work new features derived from wavelet energy and entropy are proposed. To validate the approach gait database containing data of 30 different humans is used. The obtained results are satisfactory. The classification has over than 91% accuracy.

  15. A method for the dynamic analysis of the heart using a Lyapounov based denoising algorithm.

    PubMed

    Nascimento, Jacinto C; Sanches, João M; Marques, Jorge S

    2006-01-01

    Heart tracking in ultrasound sequences is a difficult task due to speckle noise, low SNR and lack of contrast. Therefore it is usually difficult to obtain robust estimates of the heart cavities since feature detectors produce a large number of outliers. This paper presents an algorithm which combines two main operations: i) a novel denoising algorithm based on the Lyapounov equation and ii) a robust tracker, recently proposed by the authors, based on a model of the outlier features. Experimental results are provided, showing that the proposed algorithm is computationally efficient and leads to accurate estimates of the left ventricle during the cardiac cycle.

  16. Denoising of human speech using combined acoustic and em sensor signal processing

    SciTech Connect

    Ng, L C; Burnett, G C; Holzrichter, J F; Gable, T J

    1999-11-29

    Low Power EM radar-like sensors have made it possible to measure properties of the human speech production system in real-time, without acoustic interference. This greatly enhances the quality and quantify of information for many speech related applications. See Holzrichter, Burnett, Ng, and Lea, J. Acoustic. Soc. Am. 103 (1) 622 (1998). By using combined Glottal-EM- Sensor- and Acoustic-signals, segments of voiced, unvoiced, and no-speech can be reliably defined. Real-time Denoising filters can be constructed to remove noise from the user's corresponding speech signal.

  17. New methods for MRI denoising based on sparseness and self-similarity.

    PubMed

    Manjón, José V; Coupé, Pierrick; Buades, Antonio; Louis Collins, D; Robles, Montserrat

    2012-01-01

    This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings. PMID:21570894

  18. [A fast non-local means algorithm for denoising of computed tomography images].

    PubMed

    Kang, Changqing; Cao, Wenping; Fang, Lei; Hua, Li; Cheng, Hong

    2012-11-01

    A fast non-local means image denoising algorithm is presented based on the single motif of existing computed tomography images in medical archiving systems. The algorithm is carried out in two steps of prepossessing and actual possessing. The sample neighborhood database is created via the data structure of locality sensitive hashing in the prepossessing stage. The CT image noise is removed by non-local means algorithm based on the sample neighborhoods accessed fast by locality sensitive hashing. The experimental results showed that the proposed algorithm could greatly reduce the execution time, as compared to NLM, and effectively preserved the image edges and details.

  19. Video Denoising by Fuzzy Directional Filter Using the DSP EVM DM642

    NASA Astrophysics Data System (ADS)

    Gallegos-Funes, Francisco J.; Kravchenko, Victor; Ponomaryov, Volodymyr; Rosales-Silva, Alberto

    We present a new 3D Fuzzy Directional (3D-FD) algorithm for the denoising of video colour sequences corrupted by impulsive noise. The proposed approach consists of the estimations of movement levels, noise in the neighborhood video frames, permitting to preserve the edges, fine details and chromaticity characteristics in video sequences. Experimental results show that the noise in these sequences can be efficiently removed by the proposed 3D-FD filter, and that the method outperforms other state of the art filters of comparable complexity on video sequences. Finally, hardware requirements are evaluated permitting real time implementation on DSP EVM DM642.

  20. A new denoising method in high-dimensional PCA-space

    NASA Astrophysics Data System (ADS)

    Do, Quoc Bao; Beghdadi, Azeddine; Luong, Marie

    2012-03-01

    Kernel-design based method such as Bilateral filter (BIL), non-local means (NLM) filter is known as one of the most attractive approaches for denoising. We propose in this paper a new noise filtering method inspired by BIL, NLM filters and principal component analysis (PCA). The main idea here is to perform the BIL in a multidimensional PCA-space using an anisotropic kernel. The filtered multidimensional signal is then transformed back onto the image spatial domain to yield the desired enhanced image. In this work, it is demonstrated that the proposed method is a generalization of kernel-design based methods. The obtained results are highly promising.