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Sparse Reconstruction by Separable Approximation
, 2008
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing ( ..."
Abstract

Cited by 373 (36 self)
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE Journal of Selected Topics in Signal Processing
, 2007
"... Abstract—Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined wi ..."
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Cited by 524 (15 self)
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Abstract—Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined
On sparse reconstruction from Fourier and Gaussian measurements
 Communications on Pure and Applied Mathematics
, 2006
"... Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem ..."
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Cited by 265 (9 self)
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Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem
A Note on Sparse Reconstruction Methods
"... Abstract. In this paper we discuss some aspects of sparse reconstruction techniques for inverse problems, which recently became popular due to several superior properties compared to linear reconstructions. We briefly review the standard sparse reconstructions based on ℓ 1minimization of coefficien ..."
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Cited by 1 (1 self)
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Abstract. In this paper we discuss some aspects of sparse reconstruction techniques for inverse problems, which recently became popular due to several superior properties compared to linear reconstructions. We briefly review the standard sparse reconstructions based on ℓ 1minimization
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction ∗
"... We propose two algorithms based on Bregman iteration and operator splitting technique for nonlocal TV regularization problems. The convergence of the algorithms is analyzed and applications to deconvolution and sparse reconstruction are presented. 1 ..."
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Cited by 88 (9 self)
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We propose two algorithms based on Bregman iteration and operator splitting technique for nonlocal TV regularization problems. The convergence of the algorithms is analyzed and applications to deconvolution and sparse reconstruction are presented. 1
SPARSE RECONSTRUCTION BY SEPARABLE APPROXIMATION
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing ..."
Abstract
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing
DISTRIBUTED ALGORITHMS FOR SPARSE RECONSTRUCTION
"... Many applications require the knowledge of a sparse linear combination of elementary signals that can explain a given signal. This problem is known as “sparse approximation problem ” and arises in many fields of electrical engineering and applied mathematics. The great difficulty when dealing with s ..."
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Many applications require the knowledge of a sparse linear combination of elementary signals that can explain a given signal. This problem is known as “sparse approximation problem ” and arises in many fields of electrical engineering and applied mathematics. The great difficulty when dealing
Sparse reconstruction cost for abnormal event detection
 In IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2011
"... We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an overcomplete normal basis set (e.g., an image sequence or a collection of local spatiotemporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the norm ..."
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Cited by 26 (2 self)
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We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an overcomplete normal basis set (e.g., an image sequence or a collection of local spatiotemporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure
Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements
 CISS 2006 (40th Annual Conference on Information Sciences and Systems
, 2006
"... Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few nonadaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has recently ..."
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Cited by 116 (8 self)
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Abstract — This paper proves best known guarantees for exact reconstruction of a sparse signal f from few nonadaptive universal linear measurements. We consider Fourier measurements (random sample of frequencies of f) and random Gaussian measurements. The method for reconstruction that has
Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction
 IEEE Trans. Signal Process
, 2009
"... We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the du ..."
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Cited by 18 (3 self)
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We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because
Results 1  10
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101,629