Variational Bayes Approach For Tomographic Reconstruction

被引：0

作者：

Ayasso, Hacheme ^{[1
]}

Fekih-Salem, Sofia ^{[1
]}

Mohammad-Djafari, Ali ^{[1
]}

机构：

[1] Univ Paris 11, CNRS, SUPELEC, Lab Signaux & Syst UMRS 08506, F-91192 Gif Sur Yvette, France

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2008年 / 1073卷

关键词：

Variational Bayes; Tomographic reconstruction; Bayesian estimation; Gauss-Markov-Potts;

D O I：

暂无

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页码：243 / 251

页数：9

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