Model-Based Compressive Sensing

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Model-Based Compressive Sensing
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner products with M < N random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. The standard CS theory dictates that robust signal recovery is possible from M = O (K log(N/K)) measurements. The goal of this paper is to demonstrate that it is possible to substantially decrease M without sacrificing robustness by leveraging more realistic signal models that go beyond simple sparsity and compressibility by including dependencies between values and locations of the signal coefficients. We introduce a modelbased CS theory that parallels the conventional theory and provides concrete guidelines on how to create
Richard G. Baraniuk, Volkan Cevher, Marco F. Duart
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where CORR
Authors Richard G. Baraniuk, Volkan Cevher, Marco F. Duarte, Chinmay Hegde
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