Abstract / truncated to 115 words (read the full abstract)

Many signal and image processing applications have benefited remarkably from the theory of sparse representations. In its classical form this theory models signal as having a sparse representation under a given dictionary -- this is referred to as the "Synthesis Model". In this work we focus on greedy methods for the problem of recovering a signal from a set of deteriorated linear measurements. We consider four different sparsity frameworks that extend the aforementioned synthesis model: (i) The cosparse analysis model; (ii) the signal space paradigm; (iii) the transform domain strategy; and (iv) the sparse Poisson noise model. Our algorithms of interest in the first part of the work are the greedy-like schemes: CoSaMP, subspace pursuit ... toggle 12 keywords

sparsity – sparse representations – analysis – synthesis – greedy algorithms – poisson noise – denoising – near-oracle bounds – restricted isometry property – coherence – signals space methods – transform domain techniques

Follow @eurasip