Sparsity Models for Signals: Theory and Applications (2014)
Abstract / truncated to 115 words
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 keywordssparsity – sparse representations – analysis – synthesis – greedy algorithms – poisson noise – denoising – near-oracle bounds – restricted isometry property – coherence – signals space methods – transform domain techniques
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