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

Compressive sensing provides a framework for recovering sparse signals of length N from M << N measurements. If the measurements contain noise bounded by epsilon, then standard algorithms recover sparse signals with error at most C*epsilon. 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.