Probabilistic recovery guarantees for sparsely corrupted signals

Authors

Graeme Pope, Annina Bracher, and Christoph Studer

Reference

IEEE Transactions on Information Theory, 2013, to appear.

Abstract

We consider the recovery of sparse signals subject to sparse interference, as introduced in Studer et al., IEEE Trans. IT, 2012. We present novel probabilistic recovery guarantees for this framework, covering varying degrees of knowledge of the signal and interference support, which are relevant for a large number of practical applications. Our results assume that the sparsifying dictionaries are solely characterized by coherence parameters and we require randomness only in the signal and/or interference. The obtained recovery guarantees show that one can recover sparsely corrupted signals with overwhelming probability, even if the sparsity of both the signal and interference scale (near) linearly with the number of measurements.

Keywords

Sparse signal recovery, probabilistic recovery guarantees, coherence, basis pursuit, signal restoration, signal separation, compressed sensing

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