Probabilistic Recovery Guarantees for Sparsely Corrupted Signals
We consider the recovery of sparse signals subject to sparse interference, as introduced by Studer et al., IEEE T-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 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.
probabilistic recovery guarantees coherence basis pursuit compressed sensing signal restoration signal separation sparse signal recovery