A Variational Bayesian Algorithm for BSS Problem with Hidden Gauss-Markov Models for the Sources

摘要

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.