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Authors: X. Descombes 1 ; M. Lebellego 2 and E. Zhizhina 3

Affiliations: 1 Ariana Research Group, France ; 2 Ariana Research Group; Dobrushin Laboratory, Russian Federation ; 3 Dobrushin Laboratory, Russian Federation

Keyword(s): Image deconvolution, Stochastic Differential Equation, Langevin dynamics, Euler approximation.

Abstract: We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classically optimized by a MCMC sampler embeded into a simulated annealing scheme. In a previous work, we have shown that, in the context of image denoising, a diffusion process can outperform the MCMC approach in term of computational time. Herein, we extend this approach to the case of deconvolution. We first study the case where the kernel is known. Then, we address the myopic and blind deconvolutions.

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Paper citation in several formats:
Descombes, X.; Lebellego, M. and Zhizhina, E. (2007). IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH. In Proceedings of the Second International Conference on Computer Vision Theory and Applications (VISAPP 2007) - Bayesian Approach for Inverse Problems in Computer Vision; ISBN 978-972-8865-75-7; ISSN 2184-4321, SciTePress, pages 157-164. DOI: 10.5220/0002064701570164

@conference{bayesian approach for inverse problems in computer vision07,
author={X. Descombes. and M. Lebellego. and E. Zhizhina.},
title={IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications (VISAPP 2007) - Bayesian Approach for Inverse Problems in Computer Vision},
year={2007},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002064701570164},
isbn={978-972-8865-75-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications (VISAPP 2007) - Bayesian Approach for Inverse Problems in Computer Vision
TI - IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH
SN - 978-972-8865-75-7
IS - 2184-4321
AU - Descombes, X.
AU - Lebellego, M.
AU - Zhizhina, E.
PY - 2007
SP - 157
EP - 164
DO - 10.5220/0002064701570164
PB - SciTePress

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