ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 81

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

Author(s): Nadia Bali, Ali Mohammad-Djafari

Abstract

In this paper we propose a Variational Bayesian estimation approach for Blind Sources Separation problem, as an alternative method to MCMC. The data are $M$ images and the sources are $N$ images which are assumed independent and piecewise homogeneous. To insure these properties, we propose a piecewise Gauss-Markov for the sources with a hidden classification variable which is modeled via 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.

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