Stable Image Reconstruction Using Total Variation Minimization
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[1] Song Li,et al. New bounds on the restricted isometry constant δ2k , 2011 .
[2] Rachel Ward,et al. New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property , 2010, SIAM J. Math. Anal..
[3] Justin K. Romberg,et al. Restricted Isometries for Partial Random Circulant Matrices , 2010, ArXiv.
[4] R. DeVore,et al. Compressed sensing and best k-term approximation , 2008 .
[5] Tony F. Chan,et al. Total Variation Wavelet Inpainting , 2006, Journal of Mathematical Imaging and Vision.
[6] Jens Frahm,et al. Suppression of MRI Truncation Artifacts Using Total Variation Constrained Data Extrapolation , 2008, Int. J. Biomed. Imaging.
[7] J. Tropp,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.
[8] S. Osher,et al. IMAGE DECOMPOSITION AND RESTORATION USING TOTAL VARIATION MINIMIZATION AND THE H−1 NORM∗ , 2002 .
[9] Andrej Yu. Garnaev,et al. On widths of the Euclidean Ball , 1984 .
[10] S. Osher,et al. Image restoration: Total variation, wavelet frames, and beyond , 2012 .
[11] Qun Wan,et al. Total Variation Minimization Based Compressive Wideband Spectrum Sensing for Cognitive Radios , 2011, ArXiv.
[12] H. Rauhut. Compressive Sensing and Structured Random Matrices , 2009 .
[13] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[14] M. Nikolova. An Algorithm for Total Variation Minimization and Applications , 2004 .
[15] Ronald A. DeVore,et al. Image compression through wavelet transform coding , 1992, IEEE Trans. Inf. Theory.
[16] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[17] José M. Bioucas-Dias,et al. A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.
[18] Holger Rauhut,et al. Compressive Sensing with structured random matrices , 2012 .
[19] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[20] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[21] L. Ambrosio,et al. Functions of Bounded Variation and Free Discontinuity Problems , 2000 .
[22] Michael Elad,et al. The Cosparse Analysis Model and Algorithms , 2011, ArXiv.
[23] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[24] Emmanuel J. Candès,et al. Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.
[25] Holger Rauhut,et al. Sparse Legendre expansions via l1-minimization , 2012, J. Approx. Theory.
[26] R. DeVore,et al. Nonlinear Approximation and the Space BV(R2) , 1999 .
[27] Ting Sun,et al. Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..
[28] L. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .
[29] Massimo Fornasier,et al. Compressive Sensing and Structured Random Matrices , 2010 .
[30] D. Donoho,et al. Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.
[31] Emmanuel J. Candès,et al. New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction , 2002, Signal Process..
[32] Massimo Fornasier,et al. Theoretical Foundations and Numerical Methods for Sparse Recovery , 2010, Radon Series on Computational and Applied Mathematics.
[33] Deanna Needell,et al. Total variation minimization for stable multidimensional signal recovery , 2012, ArXiv.
[34] Frédéric Lesage,et al. The Application of Compressed Sensing for , 2009 .
[35] Stanley Osher,et al. Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..
[36] Rachel Ward,et al. Stable and Robust Sampling Strategies for Compressive Imaging , 2012, IEEE Transactions on Image Processing.
[37] Rama Chellappa,et al. Gradient-Based Image Recovery Methods From Incomplete Fourier Measurements , 2012, IEEE Transactions on Image Processing.
[38] Yonina C. Eldar,et al. Compressed Sensing with Coherent and Redundant Dictionaries , 2010, ArXiv.
[39] Stephen L. Keeling,et al. Total variation based convex filters for medical imaging , 2003, Appl. Math. Comput..
[40] E. Candès. The restricted isometry property and its implications for compressed sensing , 2008 .
[41] Jie Tang,et al. Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm system , 2008, SPIE Medical Imaging.
[42] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[43] S. Mendelson,et al. Uniform Uncertainty Principle for Bernoulli and Subgaussian Ensembles , 2006, math/0608665.
[44] Tom Goldstein,et al. The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..
[45] Shiqian Ma,et al. An efficient algorithm for compressed MR imaging using total variation and wavelets , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Yuval Rabani,et al. Linear Programming , 2007, Handbook of Approximation Algorithms and Metaheuristics.
[47] T. Chan,et al. Edge-preserving and scale-dependent properties of total variation regularization , 2003 .
[48] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[49] R. DeVore,et al. Nonlinear approximation and the space BV[inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="01i" /] , 1999 .
[50] Emmanuel J. Cand. The Restricted Isometry Property and Its Implications for Compressed Sensing , 2008 .
[51] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[52] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[53] Junfeng Yang,et al. A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data , 2010, IEEE Journal of Selected Topics in Signal Processing.
[54] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[55] Mohamed-Jalal Fadili,et al. Robust Sparse Analysis Regularization , 2011, IEEE Transactions on Information Theory.
[56] M. Rudelson,et al. On sparse reconstruction from Fourier and Gaussian measurements , 2008 .