Topics in sparse representation modeling and applications

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[1]  Michael Elad,et al.  Closed-Form MMSE Estimation for Signal Denoising Under Sparse Representation Modeling Over a Unitary Dictionary , 2010, IEEE Transactions on Signal Processing.

[2]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[3]  Pieter A Doevendans,et al.  Cardiovascular Ultrasound BioMed Central Review , 2006 .

[4]  T. Akasaka,et al.  Visualization of clear echocardiographic images with near field noise reduction technique: experimental study and clinical experience. , 1998, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[5]  Michael Elad,et al.  The Cosparse Analysis Model and Algorithms , 2011, ArXiv.

[6]  Michael Elad,et al.  Clutter Mitigation in Echocardiography Using Sparse Signal Separation , 2015, Int. J. Biomed. Imaging.

[7]  R. Jirik,et al.  Superresolution of ultrasound images using the first and second harmonic signal , 2004, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  M. Rudelson,et al.  Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[9]  Pradeep Ravikumar,et al.  A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation , 2012, NIPS.

[10]  S. Gupta,et al.  Wavelet-based statistical approach for speckle reduction in medical ultrasound images , 2003, Medical and Biological Engineering and Computing.

[11]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[12]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[13]  Michael Elad,et al.  Recovery of cosparse signals with Greedy Analysis Pursuit in the presence of noise , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[14]  Javier Portilla,et al.  Image restoration through l0 analysis-based sparse optimization in tight frames , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[15]  M. Vetterli,et al.  Contourlets: a new directional multiresolution image representation , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[16]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[17]  Emmanuel J. Candès,et al.  Modern statistical estimation via oracle inequalities , 2006, Acta Numerica.

[18]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[19]  Pradeep Ravikumar,et al.  BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables , 2013, NIPS.

[20]  Yin Zhang,et al.  A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation , 2010, SIAM J. Sci. Comput..

[21]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[22]  I. Yavneh,et al.  A multilevel iterated-shrinkage approach to l-1 penalized least-squares minimization , 2012 .

[23]  Edward H. Adelson,et al.  Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[24]  Trevor J. Hastie,et al.  Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso , 2011, J. Mach. Learn. Res..

[25]  K. Kristoffersen,et al.  Clutter filter design for ultrasound color flow imaging , 2002, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[26]  K. Y. Esther Leung,et al.  Probabilistic framework for tracking in artifact-prone 3D echocardiograms , 2010, Medical Image Anal..

[27]  Michael Elad,et al.  A Plurality of Sparse Representations Is Better Than the Sparsest One Alone , 2009, IEEE Transactions on Information Theory.

[28]  Lu Li,et al.  An inexact interior point method for L1-regularized sparse covariance selection , 2010, Math. Program. Comput..

[29]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[31]  Paul Tseng,et al.  A coordinate gradient descent method for nonsmooth separable minimization , 2008, Math. Program..

[32]  Irad Yavneh,et al.  A Multilevel Framework for Sparse Inverse Covariance Estimation , 2014 .

[33]  Rémi Gribonval,et al.  Iterative cosparse projection algorithms for the recovery of cosparse vectors , 2011, 2011 19th European Signal Processing Conference.

[34]  Michael Elad,et al.  L1-L2 Optimization in Signal and Image Processing , 2010, IEEE Signal Processing Magazine.

[35]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[36]  J. Arendt.  Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems , 1996 .

[37]  J D Thomas,et al.  Tissue harmonic imaging: why does it work? , 1998, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[38]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[39]  Michael Elad,et al.  Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model , 2013, IEEE Transactions on Signal Processing.

[40]  Mohamed-Jalal Fadili,et al.  Image Decomposition and Separation Using Sparse Representations: An Overview , 2010, Proceedings of the IEEE.

[41]  Michael Elad,et al.  Why Simple Shrinkage Is Still Relevant for Redundant Representations? , 2006, IEEE Transactions on Information Theory.

[42]  P. Bickel,et al.  SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.

[43]  C Caiati,et al.  Contrast-enhanced transthoracic second harmonic echo Doppler with adenosine: a noninvasive, rapid and effective method for coronary flow reserve assessment. , 1999, Journal of the American College of Cardiology.

[44]  Marc'Aurelio Ranzato,et al.  Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.

[45]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[46]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[47]  Jing Li,et al.  Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data , 2009, NIPS.

[48]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[49]  Dong Liang,et al.  Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery , 2013, IEEE Transactions on Image Processing.

[50]  Mário A. T. Figueiredo,et al.  Signal restoration with overcomplete wavelet transforms: comparison of analysis and synthesis priors , 2009, Optical Engineering + Applications.

[51]  H. Torp.  Clutter rejection filters in color flow imaging: a theoretical approach , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[52]  P J Brands,et al.  Reduction of the Clutter Component in Doppler Ultrasound Signals Based on Singular Value Decomposition: A Simulation Study , 1997, Ultrasonic imaging.

[53]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[54]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[55]  Solange Akselrod,et al.  Stationary clutter rejection in echocardiography. , 2006, Ultrasound in medicine & biology.

[56]  Justin Ziniel,et al.  Fast bayesian matching pursuit , 2008, 2008 Information Theory and Applications Workshop.

[57]  Gabriel Peyré,et al.  Learning Analysis Sparsity Priors , 2011 .

[58]  Shutao Li,et al.  Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion , 2012, IEEE Transactions on Biomedical Engineering.

[59]  Jorge Nocedal,et al.  Second Order Methods for Optimizing Convex Matrix Functions and Sparse Covariance Clustering , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[60]  Volkan Cevher,et al.  Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective , 2010, Proceedings of the IEEE.

[61]  John Gorcsan,et al.  Echocardiographic speckle tracking radial strain imaging to assess ventricular dyssynchrony in a pacing model of resynchronization therapy. , 2008, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[62]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[63]  Javier Portilla,et al.  Efficient joint poisson-gauss restoration using multi-frame L2-relaxed-L0 analysis-based sparsity , 2011, 2011 18th IEEE International Conference on Image Processing.

[64]  Alexandre d'Aspremont,et al.  First-Order Methods for Sparse Covariance Selection , 2006, SIAM J. Matrix Anal. Appl..

[65]  Michael Elad,et al.  Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization , 2007 .

[66]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[67]  Lie Wang,et al.  Stable Recovery of Sparse Signals and an Oracle Inequality , 2010, IEEE Transactions on Information Theory.

[68]  D. Kruse,et al.  A new high resolution color flow system using an eigendecomposition-based adaptive filter for clutter rejection , 2002, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[69]  Martin Kleinsteuber,et al.  Analysis Based Blind Compressive Sensing , 2013, IEEE Signal Processing Letters.

[70]  Lasse Lovstakken,et al.  Eigen-based clutter filter design for ultrasound color flow imaging: a review , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[71]  Jeff A. Bilmes,et al.  Factored sparse inverse covariance matrices , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[72]  David B. Dunson,et al.  Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images , 2012, IEEE Transactions on Image Processing.

[73]  Michael J. Black,et al.  Fields of Experts , 2009, International Journal of Computer Vision.

[74]  Alexandre d'Aspremont,et al.  Convex optimization techniques for fitting sparse Gaussian graphical models , 2006, ICML.

[75]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[76]  G.E. Trahey,et al.  A motion-based approach to abdominal clutter reduction , 2009, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[77]  Jian-Feng Cai,et al.  Split Bregman Methods and Frame Based Image Restoration , 2009, Multiscale Model. Simul..

[78]  Salvador Villarreal,et al.  High performance heart sound segmentation algorithm based on Matching Pursuit , 2013, 2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[79]  Louis-Gilles Durand,et al.  A new clutter rejection algorithm for Doppler ultrasound , 2003, IEEE Transactions on Medical Imaging.

[80]  R. Cobbold,et al.  Single-ensemble-based eigen-processing methods for color flow imaging - Part I. The Hankel-SVD filter , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[81]  Pradeep Ravikumar,et al.  Sparse inverse covariance matrix estimation using quadratic approximation , 2011, MLSLP.

[82]  Michael Elad,et al.  Analysis versus synthesis in signal priors , 2006, 2006 14th European Signal Processing Conference.

[83]  L Pourcelot,et al.  Clinical use of ultrasound tissue harmonic imaging. , 1999, Ultrasound in medicine & biology.

[84]  Li Yi,et al.  Image Super-Resolution Via Analysis Sparse Prior , 2013, IEEE Signal Processing Letters.

[85]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[86]  E. Candès,et al.  Recovering edges in ill-posed inverse problems: optimality of curvelet frames , 2002 .

[87]  Donato Mele,et al.  Improved detection of left ventricular thrombi and spontaneous echocontrast by tissue harmonic imaging in patients with myocardial infarction. , 2006, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[88]  Gregg E. Trahey,et al.  Sources and characterization of clutter in cardiac B-mode images , 2009, 2009 IEEE International Ultrasonics Symposium.

[89]  Gregg E Trahey,et al.  Adaptive Clutter Filtering via Blind Source Separation for Two-Dimensional Ultrasonic Blood Velocity Measurement , 2002, Ultrasonic imaging.

[90]  Gabriel Cristóbal,et al.  Sparse overcomplete Gabor wavelet representation based on local competitions , 2006, IEEE Transactions on Image Processing.

[91]  John A Hossack,et al.  High frequency ultrasound imaging detects cardiac dyssynchrony in noninfarcted regions of the murine left ventricle late after reperfused myocardial infarction. , 2008, Ultrasound in medicine & biology.

[92]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[93]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[94]  G. Marsaglia.  CONDITIONAL MEANS AND COVARIANCES OF NORMAL VARIABLES WITH SINGULAR COVARIANCE MATRIX , 1964 .

[95]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[96]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[97]  Wang-Q Lim,et al.  Sparse multidimensional representation using shearlets , 2005, SPIE Optics + Photonics.

[98]  D. Donoho,et al.  Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) , 2005 .

[99]  Jorge Nocedal,et al.  Newton-Like Methods for Sparse Inverse Covariance Estimation , 2012, NIPS.

[100]  R. Cobbold,et al.  Single-ensemble-based eigen-processing methods for color flow imaging - Part II. The matrix pencil estimator , 2008, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[101]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[102]  Francesco Viola,et al.  Robust motion estimation using complex principal components , 2009, 2009 IEEE International Ultrasonics Symposium.

[103]  Michael Elad,et al.  RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT , 2012, IEEE Transactions on Signal Processing.

[104]  Mohamed-Jalal Fadili,et al.  Robust Sparse Analysis Regularization , 2011, IEEE Transactions on Information Theory.

[105]  Irad Yavneh,et al.  A Multilevel Iterated-Shrinkage Approach to $l_{1}$ Penalized Least-Squares Minimization , 2012, IEEE Transactions on Signal Processing.

[106]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[107]  Yonina C. Eldar,et al.  Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery , 2010, IEEE Transactions on Signal Processing.

[108]  Michael Elad,et al.  On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary , 2011, IEEE Transactions on Signal Processing.

[109]  Michael Elad,et al.  Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[110]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .

[111]  Eran Treister,et al.  A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation , 2014, NIPS.

[112]  H. Torp,et al.  Clutter filters adapted to tissue motion in ultrasound color flow imaging , 2002, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[113]  Yeshaiahu Fainman,et al.  Image manifolds , 1998, Electronic Imaging.

[114]  A. Raftery,et al.  How Many Iterations in the Gibbs Sampler , 1991 .

[115]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[116]  Zuowei Shen,et al.  Wavelet Based Restoration of Images with Missing or Damaged Pixels , 2011 .

[117]  D. Donoho,et al.  Redundant Multiscale Transforms and Their Application for Morphological Component Separation , 2004 .

[118]  Bhabesh Deka,et al.  Removal of correlated speckle noise using sparse and overcomplete representations , 2013, Biomed. Signal Process. Control..

[119]  Rémi Gribonval,et al.  Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling , 2012, IEEE Transactions on Signal Processing.

[120]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[121]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[122]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[123]  Erik G. Larsson,et al.  Linear Regression With a Sparse Parameter Vector , 2007, IEEE Transactions on Signal Processing.

[124]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

[125]  Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.

[126]  Minh N. Do,et al.  A Theory for Sampling Signals from a Union of Subspaces , 2022 .

[127]  K. Boone,et al.  Effect of skin impedance on image quality and variability in electrical impedance tomography: a model study , 1996, Medical and Biological Engineering and Computing.

[128]  Pierre Moulin,et al.  Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian and Complexity Priors , 1999, IEEE Trans. Inf. Theory.

[129]  Yonina C. Eldar,et al.  Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.

[130]  G. Trahey,et al.  Magnitude, origins, and reduction of abdominal ultrasonic clutter , 2008, 2008 IEEE Ultrasonics Symposium.

[131]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[132]  S.T. Acton,et al.  A Transform Method to Remove Ultrasound Artifacts , 2006, 2006 IEEE Southwest Symposium on Image Analysis and Interpretation.

[133]  Michael Elad,et al.  Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary , 2008, SIAM J. Imaging Sci..

[134]  T. Loupas,et al.  On the performance of regression and step-initialized IIR clutter filters for color Doppler systems in diagnostic medical ultrasound , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[135]  Michael Elad,et al.  Patch-Ordering-Based Wavelet Frame and Its Use in Inverse Problems , 2014, IEEE Transactions on Image Processing.

[136]  Xin Li,et al.  Simultaneous Video Stabilization and Moving Object Detection in Turbulence , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[137]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[138]  Michael Elad,et al.  Cosparse analysis modeling - uniqueness and algorithms , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[139]  Tommi S. Jaakkola,et al.  Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models , 2013, UAI.

[140]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[141]  Alexandre d'Aspremont,et al.  Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .

[142]  Mohamed-Jalal Fadili,et al.  Learning the Morphological Diversity , 2010, SIAM J. Imaging Sci..

[143]  Peder A. Olsen,et al.  Nuclear Norm Minimization via Active Subspace Selection , 2014, ICML.

[144]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.

[145]  Michael Elad,et al.  On MAP and MMSE estimators for the co-sparse analysis model , 2014, Digit. Signal Process..

[146]  Scott T. Acton,et al.  A wavelet thresholding method to reduce ultrasound artifacts , 2011, Comput. Medical Imaging Graph..

[147]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[148]  Michael Elad,et al.  Morphological diversity and source separation , 2006, IEEE Signal Processing Letters.

[149]  Yonina C. Eldar,et al.  Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise , 2009, IEEE Transactions on Signal Processing.

[150]  Klaus Diepold,et al.  Analysis Operator Learning and its Application to Image Reconstruction , 2012, IEEE Transactions on Image Processing.

[151]  John A Hossack,et al.  3D perfusion mapping in post-infarct mice using myocardial contrast echocardiography. , 2006, Ultrasound in medicine & biology.

[152]  Jian-Feng Cai,et al.  Data-driven tight frame construction and image denoising , 2014 .

[153]  Jun Yan,et al.  Gaussian Markov Random Fields: Theory and Applications , 2006 .

[154]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[155]  John A. Hossack,et al.  The Singular Value Filter: A General Filter Design Strategy for PCA-Based Signal Separation in Medical Ultrasound Imaging , 2011, IEEE Transactions on Medical Imaging.

[156]  Minh N. Do,et al.  Multidimensional Directional Filter Banks and Surfacelets , 2007, IEEE Transactions on Image Processing.

[157]  Arian Maleki,et al.  Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation , 2012, NIPS.

[158]  Denis Friboulet,et al.  Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. , 2013, Ultrasonics.

[159]  Ohad Shamir,et al.  Large-Scale Convex Minimization with a Low-Rank Constraint , 2011, ICML.

[160]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[161]  Oleg V. Michailovich,et al.  A high-resolution technique for ultrasound harmonic imaging using sparse representations in Gabor frames , 2002, IEEE Transactions on Medical Imaging.

[162]  Michael Elad,et al.  On the stability of the basis pursuit in the presence of noise , 2006, Signal Process..

[163]  Michael Elad,et al.  Cosamp and SP for the cosparse analysis model , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[164]  Jafar Saniie,et al.  Model-based estimation pursuit for sparse decomposition of ultrasonic echoes , 2012, IET Signal Process..

[165]  M. West,et al.  Sparse graphical models for exploring gene expression data , 2004 .

[166]  J. Saniie,et al.  An efficient sparse signal decomposition technique for ultrasonic signal analysis using envelope and instantaneous phase , 2008, 2008 IEEE Ultrasonics Symposium.

[167]  Georg Schmitz,et al.  Compressed Sensing for Fast Image Acquisition in Pulse-Echo Ultrasound , 2012 .

[168]  Saeid Sanei,et al.  Blind Separation of Image Sources via Adaptive Dictionary Learning , 2012, IEEE Transactions on Image Processing.

[169]  Jianhua Chen,et al.  Automatic De-noising of Doppler Ultrasound Signals Using Matching Pursuit Method , 2006, ICA.

[170]  Michael Elad,et al.  Performance Guarantees of the Thresholding Algorithm for the Cosparse Analysis Model , 2013, IEEE Transactions on Information Theory.

[171]  Klaus Diepold,et al.  Cartoon-like image reconstruction via constrained ℓp-minimization , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[172]  Michael Elad,et al.  Sequential minimal eigenvalues - an approach to analysis dictionary learning , 2011, 2011 19th European Signal Processing Conference.

[173]  Michael Elad,et al.  Sparse signal separation with an off-line learned dictionary for clutter reduction in echocardiography , 2014, 2014 IEEE 28th Convention of Electrical & Electronics Engineers in Israel (IEEEI).

[174]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[175]  Rémi Gribonval,et al.  Should Penalized Least Squares Regression be Interpreted as Maximum A Posteriori Estimation? , 2011, IEEE Transactions on Signal Processing.

[176]  L. Armijo.  Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[177]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[178]  Thomas L. Griffiths,et al.  Infinite latent feature models and the Indian buffet process , 2005, NIPS.