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 language | English (US) |
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Title of host publication | Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers |
Pages | 1556-1560 |
Number of pages | 5 |
DOIs | |
State | Published - Dec 1 2009 |
Event | 43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 1 2009 → Nov 4 2009 |
Other
Other | 43rd Asilomar Conference on Signals, Systems and Computers |
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Country | United States |
City | Pacific Grove, CA |
Period | 11/1/09 → 11/4/09 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications