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Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K << N elements from an N-dimensional basis.
“On random binary trees,”
G. G. Brown and B. O. Shubert, · 1984
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“Embedded image coding using zerotrees of wavelet coefficients,”
J. Shapiro, · 1993
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“A signal-dependent time-frequency representation: Fast algorithm for optimal kernel design,”
R. G. Baraniuk and D. L. Jones, · 1994
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“Sparse approximate solutions to linear systems,”
B. K. Natarajan, · 1995
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“CART and best ortho-basis: A connection,”
D. Donoho, · 1997
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“Wavelet-based statistical signal processing using hidden Markov models,”
M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, · 1998
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“Atomic Decomposition by Basis Pursuit,”
S. S. Chen, D. L. Donoho, and M. A. Saunders, · 1998
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A Wavelet Tour of Signal Processing
S. Mallat, · 1999
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“Optimal tree approximation with wavelets,”
R. G. Baraniuk, · 1999
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“Bayesian tree-structured image modeling using wavelet-domain hidden Markov models,”
J. K. Romberg, H. Choi, and R. G. Baraniuk, · 2001
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“Tree approximation and optimal encoding,”
A. Cohen, W. Dahmen, I. Daubechies, and R. A. DeVore, · 2001
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The Concentration of Measure Phenomenon
M. Ledoux, · 2001
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“Near best tree approximation,”
R. G. Baraniuk, R. A. DeVore, G. Kyriazis, and X. M. Yu, · 2002
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“An EM algorithm for wavelet-based image restoration,”
M.A.T. Figueiredo and R.D. Nowak, · 2003
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“Image denoising using a scale mixture of Gaussians in the wavelet domain,”
J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, · 2003
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“An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,”
I. Daubechies, M. Defrise, and C. De Mol, · 2004
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“Distributed compressive sensing,”
D. Baron, M. F. Duarte, S. Sarvotham, M. B Wakin, and R. G. Baraniuk, · 2005
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“Signal recovery from random projections,”
Emmanuel J. Candès and Justin K. Romberg, · 2005
Cited alongside, same era.
“Fast reconstruction from incoherent projections,” Workshop on Sparse Representations in Redundant Systems, May 2005
R. G. Baraniuk, · 2005
Cited alongside, same era.
“Fast reconstruction of piecewise smooth signals from random projections,”
M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, · 2005
Cited alongside, same era.
“Recovery of jointly sparse signals from few random projections,”
M. B. Wakin, S. Sarvotham, M. F. Duarte, D. Baron, and R. G. Baraniuk, · 2005
Cited alongside, same era.
“Decoding by linear programming,”
E. J. Candès and T. Tao, · 2005
Cited alongside, same era.
“Compressed sensing,”
D. L. Donoho, · 2006
Cited alongside, same era.
“Sampling signals from a union of subspaces,”
Y. M. Lu and M. N. Do, · 2008
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“Iterative threhsolding for sparse approximations,”
T. Blumensath and M. E. Davies, · 2008
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“Wavelet-domain compressive signal reconstruction using a hidden Markov tree model,”
M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, · 2008
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“Selecting good Fourier measurements for compressed sensing,” SIAM Conference on Imaging Science, July 2008
K. Lee and Y. Bresler, · 2008
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“Uniform uncertainty principle for Bernoulli and subgaussian ensembles,”
S. Mendelson, A. Pajor, and N. Tomczak-Jaegermann, · 2008
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“The restricted isometry property and its implications for compressed sensing,”
E. J. Candès, · 2008
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“Compressive sampling,”
E. J. Candès, · 2006
Cited alongside, same era.
“Tree-based orthogonal matching pursuit algorithm for signal reconstruction,”
C. La and M. N. Do, · 2006
Cited alongside, same era.
“Signal reconstruction from noisy random projections,”
J. Haupt and R. Nowak, · 2006
Cited alongside, same era.
“Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit,”
D. L. Donoho, I. Drori, Y. Tsaig, and J. L. Starck, · 2006
Cited alongside, same era.
“Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit,”
J. Tropp, A. C. Gilbert, and M. J. Strauss, · 2006
Cited alongside, same era.
“Compressive sensing,”
R. G. Baraniuk, · 2007
Cited alongside, same era.
“Sampling theorems for signals from the union of finite-dimensional linear subspaces,”
T. Blumensath and M. E. Davies, · 2009
Closest in time.
“On the reconstruction of block-sparse signals with an optimal number of measurements,”
M. Stojnic, F. Parvaresh, and B. Hassibi, · 2009
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“Robust recovery of signals from a structured union of subspaces,”
Y. Eldar and M. Mishali, · 2009
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“CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,”
D. Needell and J. Tropp, · 2009
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“Iterative hard thresholding for compressed sensing,”
T. Blumensath and M. E. Davies, · 2009
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“Exploiting structure in wavelet-based Bayesian compressive sensing,”
L. He and L. Carin, · 2009
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“Subspace pursuit for compressive sensing: Closing the gap between performance and complexity,”
W. Dai and O. Milenkovic, · 2009
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“Model-based compressive Sensing for Signal Ensembles,”
M. F. Duarte, V. Cevher, and R. G. Baraniuk, · 2009
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“Compressive sensing recovery of spike trains using a structured sparsity model,”
C. Hegde, M. F. Duarte, and V. Cevher, · 2009
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“Recovery of clustered sparse signals from compressive measurements,”
V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, · 2009
Closest in time.