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TV-min and greedy
pursuit for constrained joint sparsity and application to
inverse scattering
Albert Fannjiang
Vol. 1 (2013), No. 1, 81–104
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Abstract |
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This paper proposes a general framework for compressed sensing of constrained joint sparsity (CJS) which includes total variation minimization (TV-min) as an example. The gradient- and 2-norm error bounds, independent of the ambient dimension, are derived for the CJS version of basis pursuit and orthogonal matching pursuit. As an application the results extend Candès, Romberg, and Tao’s proof of exact recovery of piecewise constant objects with noiseless incomplete Fourier data to the case of noisy data. |
Keywords
total variation, joint sparsity, multiple measurement
vectors, compressive sensing
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Mathematical Subject Classification 2010
Primary: 15A29
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Milestones
Received: 11 May 2012
Revised: 17 September 2012
Accepted: 12 November 2012
Published: 6 February 2013
Communicated by Micol Amar
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Authors |
| Albert Fannjiang | |
| Department of Mathematics University of California, Davis One Shields Ave. Davis, CA 95616-8633 United States |
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