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Proceedings Paper

Wavelet domain blind image separation
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Paper Abstract

In this work, we consider the problem of blind source separation in the wavelet domain via a Bayesian estimation framework. We use the sparsity and multiresolution properties of the wavelet coefficients to model their distribution by heavy tailed prior probability laws: the generalized exponential family and the Gaussian mixture family. Appropriate MCMC algorithms are developed in each case for the estimation purposes and simulation results are presented for comparaison.

Paper Details

Date Published: 13 November 2003
PDF: 10 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.508134
Show Author Affiliations
Mahieddine M. Ichir, LSS/Ecole Superieure d'Electricte (France)
Ali Mohammad-Djafari, LSS/Ecole Superieure d'Electricte (France)

Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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