Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine
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[1] Gene H. Golub,et al. A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration , 1999, SIAM J. Sci. Comput..
[2] Rémi Gribonval,et al. Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$ , 2009, IEEE Transactions on Information Theory.
[3] V.K. Goyal,et al. Compressive Sampling and Lossy Compression , 2008, IEEE Signal Processing Magazine.
[4] Richard G. Baraniuk,et al. 1-Bit compressive sensing , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.
[5] Pierre Vandergheynst,et al. Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.
[6] Junfeng Yang,et al. A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..
[7] Emmanuel J. Candès,et al. Encoding the /spl lscr//sub p/ ball from limited measurements , 2006, Data Compression Conference (DCC'06).
[8] Piotr Indyk,et al. Sparse Recovery Using Sparse Matrices , 2010, Proceedings of the IEEE.
[9] Hong-Kun Xu. Inequalities in Banach spaces with applications , 1991 .
[10] Restricted Isometry Constants where p sparse , 2011 .
[11] W. Hoeffding. Probability Inequalities for sums of Bounded Random Variables , 1963 .
[12] Laurent Jacques,et al. DeQuantizing Compressed Sensing with non-Gaussian constraints , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[13] R. DeVore,et al. Compressed sensing and best k-term approximation , 2008 .
[14] M. Varanasi,et al. Parametric generalized Gaussian density estimation , 1989 .
[15] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[16] S. Foucart,et al. Sparsest solutions of underdetermined linear systems via ℓq-minimization for 0 , 2009 .
[17] Martin Vetterli,et al. Deterministic analysis of oversampled A/D conversion and decoding improvement based on consistent estimates , 1994, IEEE Trans. Signal Process..
[18] D. Donoho,et al. Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.
[19] Piotr Indyk,et al. Combining geometry and combinatorics: A unified approach to sparse signal recovery , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.
[20] P. L. Combettes,et al. Solving monotone inclusions via compositions of nonexpansive averaged operators , 2004 .
[21] R. Spira. Calculation of the Gamma Function by Stirling's Formula , 1971 .
[22] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[23] Olgica Milenkovic,et al. Quantized Compressive Sensing , 2009, 0901.0749.
[24] Richard G. Baraniuk,et al. Exact signal recovery from sparsely corrupted measurements through the Pursuit of Justice , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.
[25] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[26] M. Nikolova. An Algorithm for Total Variation Minimization and Applications , 2004 .
[27] M. Ledoux. The concentration of measure phenomenon , 2001 .
[28] Daniel Zwillinger,et al. CRC standard mathematical tables and formulae; 30th edition , 1995 .
[29] Richard G. Baraniuk,et al. Quantization of Sparse Representations , 2007, 2007 Data Compression Conference (DCC'07).
[30] R. D. Carmichael,et al. Mathematical tables and formulae , 1958 .
[31] Emmanuel J. Cand. The Restricted Isometry Property and Its Implications for Compressed Sensing , 2008 .
[32] Jean-Jacques Fuchs,et al. Fast implementation of a ℓ1 - ℓ1 regularized sparse representations algorithm. , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[33] Davies Rémi Gribonval. Restricted Isometry Constants Where Lp Sparse Recovery Can Fail for 0 , 2008 .
[34] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[35] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[36] D. Donoho,et al. Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.
[37] Stephen P. Boyd,et al. Compressed Sensing With Quantized Measurements , 2010, IEEE Signal Processing Letters.
[38] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[39] E. Candès. The restricted isometry property and its implications for compressed sensing , 2008 .
[40] Marc Antonini,et al. Compression artifacts reduction using variational methods : Algorithms and experimental study , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[41] Patrick L. Combettes,et al. Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..
[42] Richard G. Baraniuk,et al. Democracy in Action: Quantization, Saturation, and Compressive Sensing , 2011 .
[43] E. Candès,et al. Encoding the ` p Ball from Limited Measurements , 2006 .
[44] W. L. Bynum,et al. Weak Parallelogram Laws for Banach Spaces , 1976, Canadian Mathematical Bulletin.
[45] W. Beyer. CRC Standard Mathematical Tables and Formulae , 1991 .
[46] P. L. Combettes,et al. A proximal decomposition method for solving convex variational inverse problems , 2008, 0807.2617.
[47] Curtis R. Vogel,et al. Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..
[48] S. Mendelson,et al. Reconstruction and Subgaussian Operators in Asymptotic Geometric Analysis , 2007 .
[49] Olgica Milenkovic,et al. Distortion-rate functions for quantized compressive sensing , 2009, 2009 IEEE Information Theory Workshop on Networking and Information Theory.
[50] J. Moreau. Fonctions convexes duales et points proximaux dans un espace hilbertien , 1962 .
[51] R. Chartrand,et al. Restricted isometry properties and nonconvex compressive sensing , 2007 .
[52] Leslie Ying,et al. Linear transformations and Restricted Isometry Property , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[53] Michel Verleysen,et al. The Concentration of Fractional Distances , 2007, IEEE Transactions on Knowledge and Data Engineering.
[54] Emmanuel J. Candès,et al. Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..