A variational Bayesian algorithm for BSS problem with hidden Gauss-Markov models for the sources

  • Authors:

  • Nadia Bali;Ali Mohammad-Djafari

  • Affiliations:

  • Laboratoire des Signaux et Systèmes, Unité mixte de recherche, CNRS-Supélec-UPS, Supélec, Gif-sur-Yvette, France;Laboratoire des Signaux et Systèmes, Unité mixte de recherche, CNRS-Supélec-UPS, Supélec, Gif-sur-Yvette, France

  • Venue:
  • ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
  • Year:
  • 2007

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Abstract

In this paper we propose a Variational Bayesian (VB) estimation approach for Blind Sources Separation (BSS) problem, as an alternative method to MCMC. The data are M images and the sources are N images which are assumed piecewise homogeneous. To insure these properties, we propose a piecewise Gauss-Markov model for the sources with a hidden classification variable which is modeled by a Potts-Markov field. A few simulation results are given to illustrate the performances of the proposed method and some comparison with other methods (MCMC and VBICA) used for BSS, are presented.