Bayesian Wavelet Based Signal and Image Separation

Abstract

In this contribution, we consider the problem of blind source separation in a Bayesian estimation framework. The wavelet representation allows us to assign an adequate prior distribution to the wavelet coefficients of the sources. MCMC algorithms are implemented to test the validity of the proposed approach, and the non linear approximation of the wavelet transform is exploited to aleviate the algorithm.

Int. Conf. on Bayesian Inference and Maximum Entropy Methods (Maxent 2003) Jackson Hole (WY), USA. 12 page, 6 figures