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Authors: Hacheme Ayasso and Ali Mohammad-Djafari

Affiliation: Laboratoire des Signaux et Syste`mes, UMR 8506 (CNRS-SUPELEC-UPS), SUPELEC, France

Keyword(s): Variational Bayes Approximation, Image Restoration, Bayesian estimation, MCMC.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Enhancement and Restoration ; Image Formation and Preprocessing

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.

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Paper citation in several formats:
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 (VISIGRAPP 2008) - Volume 1: BAIPCV; ISBN 978-989-8111-21-0; ISSN 2184-4321, SciTePress, pages 571-576. DOI: 10.5220/0001091805710576

@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 (VISIGRAPP 2008) - Volume 1: BAIPCV},
year={2008},
pages={571-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001091805710576},
isbn={978-989-8111-21-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: BAIPCV
TI - VARIATIONAL BAYES WITH GAUSS-MARKOV-POTTS PRIOR MODELS FOR JOINT IMAGE RESTORATION AND SEGMENTATION
SN - 978-989-8111-21-0
IS - 2184-4321
AU - Ayasso, H.
AU - Mohammad-Djafari, A.
PY - 2008
SP - 571
EP - 576
DO - 10.5220/0001091805710576
PB - SciTePress

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