Staistical-mechanical iterative algorithm by means of cluster variation method in compound Gauss-Markov random field model

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4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)259-267
Number of pages9
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume16
Issue number2
DOIs
Publication statusPublished - 2001 Dec 1

Keywords

  • Bayes statistics
  • Cluster variation method
  • Compound Gauss-Markov random
  • Field model
  • Image restoration
  • Markov random fields
  • Maximum a posteriori estimation
  • Maximum posterior marginal estimation
  • Mean-field approximation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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