Stable signal recovery from incomplete and inaccurate measurements
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[1] Stanislaw J. Szarek,et al. Condition numbers of random matrices , 1991, J. Complex..
[2] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[3] Ronald A. DeVore,et al. Image compression through wavelet transform coding , 1992, IEEE Trans. Inf. Theory.
[4] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[5] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[6] 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.
[7] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[8] A. Gilbert,et al. Theoretical and experimental analysis of a randomized algorithm for Sparse Fourier transform analysis , 2004, math/0411102.
[9] Wotao Yin,et al. Second-order Cone Programming Methods for Total Variation-Based Image Restoration , 2005, SIAM J. Sci. Comput..
[10] Anna C. Gilbert,et al. Improved time bounds for near-optimal sparse Fourier representations , 2005, SPIE Optics + Photonics.
[11] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[12] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[13] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[14] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[15] Michael Elad,et al. Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.
[16] Emmanuel J. Candès,et al. Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..
[17] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[18] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[19] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.