Joint Image Restoration and Segmentation using Gauss-Markov-Potts Prior Models and Variational Bayesian Computation

Abstract : In this paper, we propose a method to restore and to segment simultaneously images degraded by a known point spread function (PSF) and additive white noise. For this purpose, we propose a joint Bayesian estimation framework, where a family of non-homogeneous Gauss-Markov fields with Potts region labels models are chosen to serve as priors for images. Since neither the joint maximum a posteriori estimator nor posterior mean one are tractable, the joint posterior law of the image, its segmentation and all the hyper-parameters, is approximated by a separable probability laws using the Variational Bayes technique. This yields a known probability laws of the posterior with mutually dependent shaping parameter, which aims to enhance the convergence speed of the estimator com- pared to stochastic sampling based estimator. Practical results are presented with comparison to a MCMC based estimator.
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https://hal.archives-ouvertes.fr/hal-00444694
Contributor : Hacheme Ayasso <>
Submitted on : Thursday, January 7, 2010 - 10:45:08 AM
Last modification on : Thursday, April 5, 2018 - 12:30:04 PM

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  • HAL Id : hal-00444694, version 1

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Hacheme Ayasso, Ali Mohammad-Djafari. Joint Image Restoration and Segmentation using Gauss-Markov-Potts Prior Models and Variational Bayesian Computation. the 15th IEEE International Conference on Image Processing, (ICIP), Nov 2009, Cairo, Egypt. pp.1297--1300. ⟨hal-00444694⟩

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