暂无分享,去创建一个
Michael Elad | Rémi Gribonval | Mike E. Davies | Sangnam Nam | M. Davies | R. Gribonval | Michael Elad | Sangnam Nam | M. Davies
[1] Michael Elad,et al. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.
[2] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[3] Michael Elad,et al. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..
[4] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[5] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[6] Mário A. T. Figueiredo,et al. Signal restoration with overcomplete wavelet transforms: comparison of analysis and synthesis priors , 2009, Optical Engineering + Applications.
[7] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[8] Stéphane Mallat,et al. Zero-crossings of a wavelet transform , 1991, IEEE Trans. Inf. Theory.
[9] D. Donoho,et al. Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.
[10] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[11] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[12] Horst Bischof,et al. Exploiting Redundancy for Aerial Image Fusion Using Convex Optimization , 2010, DAGM-Symposium.
[13] Jian-Feng Cai,et al. Split Bregman Methods and Frame Based Image Restoration , 2009, Multiscale Model. Simul..
[14] Mike E. Davies,et al. Sampling Theorems for Signals From the Union of Finite-Dimensional Linear Subspaces , 2009, IEEE Transactions on Information Theory.
[15] 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).
[16] Michael Elad,et al. Cosparse analysis modeling - uniqueness and algorithms , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[17] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[18] D. Donoho,et al. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) , 2005 .
[19] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[20] Michael Elad,et al. Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .
[21] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[22] Jos F. Sturm,et al. A Matlab toolbox for optimization over symmetric cones , 1999 .
[23] Thomas Blumensath,et al. Accelerated iterative hard thresholding , 2012, Signal Process..
[24] Vladimir N. Temlyakov,et al. Weak greedy algorithms[*]This research was supported by National Science Foundation Grant DMS 9970326 and by ONR Grant N00014‐96‐1‐1003. , 2000, Adv. Comput. Math..
[25] Fionn Murtagh,et al. Gray and color image contrast enhancement by the curvelet transform , 2003, IEEE Trans. Image Process..
[26] Michael B. Wakin. Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews] , 2011, IEEE Signal Processing Magazine.
[27] Emmanuel J. Candès,et al. The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).
[28] R. Gribonval,et al. Highly sparse representations from dictionaries are unique and independent of the sparseness measure , 2007 .
[29] J. Tropp. JUST RELAX: CONVEX PROGRAMMING METHODS FOR SUBSET SELECTION AND SPARSE APPROXIMATION , 2004 .
[30] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[31] Michael Elad,et al. Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..
[32] Mohamed-Jalal Fadili,et al. A Generalized Forward-Backward Splitting , 2011, SIAM J. Imaging Sci..
[33] Martin Vetterli,et al. Wavelet footprints: theory, algorithms, and applications , 2003, IEEE Trans. Signal Process..
[34] Olgica Milenkovic,et al. Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.
[35] 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).
[36] Michael Elad,et al. Analysis versus synthesis in signal priors , 2006, 2006 14th European Signal Processing Conference.
[37] Pierre Vandergheynst,et al. Blind Audiovisual Source Separation Based on Sparse Redundant Representations , 2010, IEEE Transactions on Multimedia.
[38] S. Mallat. A wavelet tour of signal processing , 1998 .
[39] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[40] Wang-Q Lim,et al. Sparse multidimensional representation using shearlets , 2005, SPIE Optics + Photonics.
[41] Kjersti Engan,et al. Multi-frame compression: theory and design , 2000, Signal Process..
[42] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[43] I. Daubechies,et al. Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.
[44] S. Mallat,et al. Adaptive greedy approximations , 1997 .
[45] D. Donoho,et al. Redundant Multiscale Transforms and Their Application for Morphological Component Separation , 2004 .
[46] Minh N. Do,et al. A Theory for Sampling Signals from a Union of Subspaces , 2022 .
[47] Rémi Gribonval,et al. Sparse Representations in Audio and Music: From Coding to Source Separation , 2010, Proceedings of the IEEE.
[48] Yonina C. Eldar,et al. Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.
[49] Kjersti Engan,et al. Recursive Least Squares Dictionary Learning Algorithm , 2010, IEEE Transactions on Signal Processing.
[50] Bhaskar D. Rao,et al. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..
[51] Vishal M. Patel. Sparse and Redundant Representations for Inverse Problems and Recognition , 2010 .
[52] Stphane Mallat,et al. A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way , 2008 .
[53] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[54] Minh N. Do,et al. Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .
[55] David S. Johnson,et al. Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .
[56] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[57] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[58] Yonina C. Eldar,et al. Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise , 2009, IEEE Transactions on Signal Processing.