IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH
X. Descombes, M. Lebellego, E. Zhizhina
2007
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.
References
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Paper Citation
in Harvard Style
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 - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007) ISBN 978-972-8865-75-7, pages 157-164. DOI: 10.5220/0002064701570164
in Bibtex Style
@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 - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007)},
year={2007},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002064701570164},
isbn={978-972-8865-75-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Bayesian Approach for Inverse Problems in Computer Vision, (VISAPP 2007)
TI - IMAGE DECONVOLUTION USING A STOCHASTIC DIFFERENTIAL EQUATION APPROACH
SN - 978-972-8865-75-7
AU - Descombes X.
AU - Lebellego M.
AU - Zhizhina E.
PY - 2007
SP - 157
EP - 164
DO - 10.5220/0002064701570164