Sparsity Models for Signals: Theory and Applications

Raja Giryes, Ph.D. Thesis Seminar
Tuesday, 15.10.2013, 11:30
Taub 337
Prof. M. Elad

Many signal and image processing applications have benefited remarkably from the theory of sparse representations. In the classical synthesis model, the signal is assumed to have a sparse representation under a given dictionary. 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 that target the signal's representation: (i) The cosparse analysis model; (ii) the signal space paradigm; (iii) the transform domain strategy; and (iv) and the sparse Poisson noise model. In the first part of the talk we present extensions for greedy-like algorithms for the synthesis and the first three alternative models. In the second part we consider the Poisson denoising problem with a new Poisson statistics based sparsity model achieving state-of-the-art-results.

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