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

Jason N. Laska, Mark A. Davenport, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

93 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages1556-1560
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 1 2009Nov 4 2009

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/1/0911/4/09

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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