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1 Institut Langevin ondes et images 2 LTCI - Laboratoire Traitement et Communication de l-Information 3 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences CEMAGREF, Inria Rennes – Bretagne Atlantique

Abstract : Taking advantage of the structures inherent in many sparse decompositions constitutes a promising research axis. In this paper, we address this problem from a Bayesian point of view. We exploit a Boltzmann machine, allowing to take a large variety of structures into account, and focus on the resolution of a marginalized maximum a posteriori problem. To solve this problem, we resort to a mean-field approximation and the variational Bayes Expectation-Maximization- algorithm. This approach results in a soft procedure making no hard decision in the support or the values of the sparse representation. We show that this characteristic leads to an improvement of the performance over state-of-the-art algorithms.

Keywords : mean-field approximation Structured sparse representation Bernoulli-Gaussian model Boltzmann machine mean-field approximation.

Autor: Angélique Drémeau - Cédric Herzet - Laurent Daudet -



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