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Enhancing Sparsity by Reweighted ℓ1 Minimization
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done byExpand
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A Simple Proof of the Restricted Isometry Property for Random Matrices
Abstract We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two mainExpand
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Signal Processing With Compressive Measurements
TLDR
The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. Expand
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Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property
  • M. Davenport, M. Wakin
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 September 2009
TLDR
We demonstrate that the restricted isometry property (RIP) can be used for a very straightforward analysis of Orthogonal matching pursuit (OMP). Expand
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Distributed Compressive Sensing
TLDR
In this paper we introduce a new theory for distributed compressive sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter- signal correlation structures. Expand
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An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition]
TLDR
This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. Expand
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Distributed Compressed Sensing Dror
TLDR
In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intraand inter-Signal correlation structures and characterize the number of measurements per sensor required for accurate reconstruction. Expand
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A new compressive imaging camera architecture using optical-domain compression
TLDR
In this paper, we develop a new camera architecture that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Expand
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Random Projections of Smooth Manifolds
TLDR
We propose a new approach for nonadaptive dimensionality reduction of manifold-modeled data, demonstrating that a small number of random linear projections can preserve key information about a manifold-Modeled signal. Expand
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Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (Starck, J.-L., et al; 2010) [Book Reviews]
  • M. Wakin
  • Computer Science
  • IEEE Signal Processing Magazine
  • 30 August 2011
TLDR
Survey of sparse representations and wavelet transforms for sparse representations . Expand
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