TV-min and Greedy Pursuit for Constrained Joint Sparsity and Application to Inverse Scattering
Optimization and Control
2012-12-20 v3
Abstract
This paper proposes a general framework for compressed sensing of constrained joint sparsity (CJS) which includes total variation minimization (TV-min) as an example. TV- 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 Cand`es, Romberg and Tao's proof of exact recovery of piecewise constant objects with noiseless incomplete Fourier data to the case of noisy data.
Keywords
Cite
@article{arxiv.1205.3834,
title = {TV-min and Greedy Pursuit for Constrained Joint Sparsity and Application to Inverse Scattering},
author = {Albert Fannjiang},
journal= {arXiv preprint arXiv:1205.3834},
year = {2012}
}
Comments
Mathematics and Mechanics of Complex Systems (2013)