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Lower Bounds for Sparse Recovery
"... We consider the following ksparse recovery problem: design an m × n matrix A, such that for any signal x, given Ax we can efficiently recover ˆx satisfying ..."
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Cited by 60 (23 self)
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We consider the following ksparse recovery problem: design an m × n matrix A, such that for any signal x, given Ax we can efficiently recover ˆx satisfying
On the Power of Adaptivity in Sparse Recovery
, 2011
"... The goal of (stable) sparse recovery is to recover a ksparse approximation x ∗ of a vector x from linear measurements of x. Specifically, the goal is to recover x ∗ such that ‖x − x ∗ ‖ ..."
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Cited by 14 (4 self)
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The goal of (stable) sparse recovery is to recover a ksparse approximation x ∗ of a vector x from linear measurements of x. Specifically, the goal is to recover x ∗ such that ‖x − x ∗ ‖
Sparse Recovery Using Sparse Matrices
"... We survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas ..."
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Cited by 74 (12 self)
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We survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several
Sparse Recovery and Fourier Sampling
, 2013
"... the last decade a broad literature has arisen studying sparse recovery, the estimation of sparse vectors from low dimensional linear projections. Sparse recovery has a wide variety of applications such as streaming algorithms, image acquisition, and disease testing. A particularly important subclass ..."
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Cited by 1 (0 self)
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the last decade a broad literature has arisen studying sparse recovery, the estimation of sparse vectors from low dimensional linear projections. Sparse recovery has a wide variety of applications such as streaming algorithms, image acquisition, and disease testing. A particularly important
Sparse Recovery in Inverse Problems
 RADON SERIES COMP. APPL. MATH XX, 1–63
"... Within this chapter we present recent results on sparse recovery algorithms for inverse and illposed problems, i.e. we focus on those inverse problems in which we can assume that the solution has a sparse series expansion with respect to a preassigned basis or frame. The presented approaches to a ..."
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Within this chapter we present recent results on sparse recovery algorithms for inverse and illposed problems, i.e. we focus on those inverse problems in which we can assume that the solution has a sparse series expansion with respect to a preassigned basis or frame. The presented approaches
AN ALPS VIEW OF SPARSE RECOVERY
"... We provide two compressive sensing (CS) recovery algorithms based on iterative hardthresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra of Nesterov’s optimal gradient methods. We th ..."
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Cited by 15 (7 self)
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We provide two compressive sensing (CS) recovery algorithms based on iterative hardthresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra of Nesterov’s optimal gradient methods. We
Sequential Testing for Sparse Recovery
, 2012
"... This paper studies sequential methods for recovery of sparse signals in high dimensions. When compared to fixed sample size procedures, in the sparse setting, sequential methods can result in a particularly large reduction in the number of samples needed for reliable signal support recovery. Startin ..."
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Cited by 5 (0 self)
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This paper studies sequential methods for recovery of sparse signals in high dimensions. When compared to fixed sample size procedures, in the sparse setting, sequential methods can result in a particularly large reduction in the number of samples needed for reliable signal support recovery
Sparse Recovery for Discrete Tomography
 in Prof. of ICIP 2010, Hong Kong
, 2010
"... Discrete tomography (DT) focuses on the reconstruction of a discrete valued image from few projection angles. Prior knowledge about the image can greatly increase the quality of the reconstructed image, especially when a small number of projections are available. In this paper, we show that DT can ..."
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Cited by 2 (1 self)
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be formulated as a sparse signal recovery problem. By using a well designed dictionary, it is possible to represent a binary image with very few coefficients. Starting from this concept, we modify the reweighed l1 algorithm to achieve a sparse solution and preserve the binary property of image. Pre
Results 1  10
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