VARIATIONAL BAYES WITH GAUSS-MARKOV-POTTS PRIOR MODELS FOR JOINT IMAGE RESTORATION AND SEGMENTATION

Hacheme Ayasso, Ali Mohammad-Djafari

2008

Abstract

In this paper, we propose a family of non-homogeneous Gauss-Markov fields with Potts region labels model for images to be used in a Bayesian estimation framework, in order to jointly restore and segment images degraded by a known point spread function and additive noise. The joint posterior law of all the unknowns ( the unknown image, its segmentation hidden variable and all the hyperparameters) is approximated by a separable probability laws via the variational Bayes technique. This approximation gives the possibility to obtain practically implemented joint restoration and segmentation algorithm. We will present some preliminary results and comparison with a MCMC Gibbs sampling based algorithm.

Paper Citation

in Harvard Style

Ayasso H. and Mohammad-Djafari A. (2008). VARIATIONAL BAYES WITH GAUSS-MARKOV-POTTS PRIOR MODELS FOR JOINT IMAGE RESTORATION AND SEGMENTATION . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: BAIPCV, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 571-576. DOI: 10.5220/0001091805710576

in Bibtex Style

@conference{baipcv08,
author={Hacheme Ayasso and Ali Mohammad-Djafari},
title={VARIATIONAL BAYES WITH GAUSS-MARKOV-POTTS PRIOR MODELS FOR JOINT IMAGE RESTORATION AND SEGMENTATION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: BAIPCV, (VISIGRAPP 2008)},
year={2008},
pages={571-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001091805710576},
isbn={978-989-8111-21-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: BAIPCV, (VISIGRAPP 2008)
TI - VARIATIONAL BAYES WITH GAUSS-MARKOV-POTTS PRIOR MODELS FOR JOINT IMAGE RESTORATION AND SEGMENTATION
SN - 978-989-8111-21-0
AU - Ayasso H.
AU - Mohammad-Djafari A.
PY - 2008
SP - 571
EP - 576
DO - 10.5220/0001091805710576