Exact signal recovery from sparsely corrupted measurements through the pursuit of justice

Authors:
Jason N. Laska;Mark A. Davenport;Richard G. Baraniuk
Affiliations:
Department of Electrical and Computer Engineering, Rice University, Houston, Texas;Department of Electrical and Computer Engineering, Rice University, Houston, Texas;Department of Electrical and Computer Engineering, Rice University, Houston, Texas
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

Compressive sensing provides a framework for recovering sparse signals of length N from M ≪ N measurements. If the measurements contain noise bounded by ε, then standard algorithms recover sparse signals with error at most Cε. However, these algorithms perform suboptimally when the measurement noise is also sparse. This can occur in practice due to shot noise, malfunctioning hardware, transmission errors, or narrowband interference. We demonstrate that a simple algorithm, which we dub Justice Pursuit (JP), can achieve exact recovery from measurements corrupted with sparse noise. The algorithm handles unbounded errors, has no input parameters, and is easily implemented via standard recovery techniques.