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- 田中 和之
- 東北大学大学院情報科学研究科情報基礎科学専攻
書誌事項
- タイトル別名
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- Staistical-mechanical Iterative Algorithm by means of Cluster Variation Method in Compound Gauss-Markov Random Field Model
- ケツゴウ ガウス マルコフ カクリツバ モデル ニ タイスル クラスター ヘン ブンホウ ニ ヨル トウケイ リキガクテキ ハンプク ケイサン アルゴリズム
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説明
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.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 16 259-267, 2001
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390001205108172800
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- NII論文ID
- 10015769916
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- NII書誌ID
- AA11579226
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- ISSN
- 13468030
- 13460714
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- NDL書誌ID
- 5987164
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- 本文言語コード
- ja
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- NDLサーチ
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