Joint image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation

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 lab...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 1297 - 1300
Main Authors Ayasso, H., Mohammad-Djafari, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Additive white noise
Bayes procedures
Bayesian methods
Convergence
Degradation
Gaussian processes
Image restoration
Image segmentation
Maximum a posteriori estimation
Stochastic processes
Variational Bayes Approximation
Yield estimation
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Summary: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 compared to stochastic sampling based estimator. Practical results are presented with comparison to a MCMC based estimator.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5413589