Variational Bayes Approach For Tomographic Reconstruction

In this paper, we apply the Bayesian inference method in a tomographic reconstruction problem. For this purpose, we propose a Gauss-Markov field with Potts region label model for the images. Most of model parameters are unknown and we wish to estimate them jointly with the object of interest. Using...

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Published inBayesian Inference and Maximum Entropy Methods in Science and Engineering (28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering) (AIP Conference Proceedings Volume 1073) Vol. 1073; pp. 243 - 251
Main Authors Ayasso, Hacheme, Fekih-Salem, Sofia, Mohammad-Djafari, Ali
Format Journal Article
LanguageEnglish
Published 01.01.2008
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Summary:In this paper, we apply the Bayesian inference method in a tomographic reconstruction problem. For this purpose, we propose a Gauss-Markov field with Potts region label model for the images. Most of model parameters are unknown and we wish to estimate them jointly with the object of interest. Using the variational Bayes framework, the joint posterior law is approximated by a product of marginal laws whose shaping parameter equations are derived. An application to tomographic reconstruction is presented with discussion of convergence and quality of this estimation.
Bibliography:SourceType-Scholarly Journals-2
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ObjectType-Conference Paper-1
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ISBN:0735406049
9780735406049
ISSN:0094-243X
DOI:10.1063/1.3039006