文章基本信息

标题：Staistical-mechanical Iterative Algorithm by means of Cluster Variation Method in Compound Gauss-Markov Random Field Model
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作者：Kazuyuki Tanaka
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2001
卷号：16
期号：2
页码：259-267
DOI：10.1527/tjsai.16.259
出版社：The Japanese Society for Artificial Intelligence
摘要：Compound Gauss-Markov random field model is one of Markov random field models for natural image restorations. An optimization algorithm was constructed by means of mean-field approximation, which is a familiar techniques for analyzing massive probabilistic models approximately in the statistical mechanics. Cluster variation method was proposed as an extended version of the mean-field approximation in the statistical mechanics. Though the mean-field approximation treat only the marginal probability distribution for every single pixel, the cluster variation method can take acount into the correlation between pixels by treating the marginal probability distribution for every nearest neighbor pair of pixels. In this paper, we propose a newstatistical-mechanical iterative algorithm by means of the cluster variation method for natural image restorations in the compound Gauss-Markov random field model. In some numerical experiments, it is investigate howthe proposed algorithm improves the quality of restored images by comparing it with the algorithm constructed from the mean-field approximation.
关键词：Markov random fields ; image restoration ; mean-field approximation ; compound Gauss-Markov random ; field model ; Bayes statistics ; maximum a posteriori estimation ; maximum posterior marginal estimation ; cluster variation method