Variational Bayes Approach For Tomographic Reconstruction

Abstract : 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
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https://hal.archives-ouvertes.fr/hal-00446713
Contributor : Hacheme Ayasso <>
Submitted on : Wednesday, January 13, 2010 - 1:53:32 PM
Last modification on : Wednesday, January 23, 2019 - 2:38:28 PM

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Hacheme Ayasso, Sofia Fekih-Salem, Ali Mohammad-Djafari. Variational Bayes Approach For Tomographic Reconstruction. the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt, Jul 2008, Sao Paulo, Brazil. pp.243--251, ⟨10.1063/1.3039006⟩. ⟨hal-00446713⟩

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