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Soft Bayesian Pursuit Algorithm for Sparse Representations

Angélique Drémeau 1, 2 Cedric Herzet 3 Laurent Daudet 1
3 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
CEMAGREF - Centre national du machinisme agricole, du génie rural, des eaux et forêts, Inria Rennes – Bretagne Atlantique
Abstract : This paper deals with sparse representations within a Bayesian framework. For a Bernoulli-Gaussian model, we here propose a method based on a mean-field approximation to estimate the support of the signal. In numerical tests involving a recovery problem, the resulting algorithm is shown to have good performance over a wide range of sparsity levels, compared to various state-of-the-art algorithms.
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Submitted on : Monday, May 14, 2012 - 12:07:30 PM
Last modification on : Thursday, November 18, 2021 - 4:13:40 AM
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  • HAL Id : hal-00696898, version 1


Angélique Drémeau, Cedric Herzet, Laurent Daudet. Soft Bayesian Pursuit Algorithm for Sparse Representations. IEEE Workshop on Statistical Signal Processing, Jun 2011, Nice, France. ⟨hal-00696898⟩



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