Image Restoration and Segmentation using Region-Based Latent Variables: Bayesian Inference Based on Variational Method
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概要
- 論文の詳細を見る
To represent edges in image processing based on Bayesian inference, it is very effective to introduce latent variables. In this paper, we derive a deterministic algorithm that restores and segments an image using region-based latent variables and variational inference. This algorithm estimates two hyperparameters as well as infers the original image and the latent variables. In addition, the algorithm carries out model selection by minimizing the variational free energy. Through experiments using an artificial image generated by the heat bath method and natural images degraded by Gaussian noises, the effectiveness and limitations of the derived algorithm are shown.
- Physical Society of Japanの論文
- 2011-01-15
著者
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Okada Masato
Division Of Protein Metabolism Institute For Protein Research Osaka University
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Okada Masato
Division of Transdisciplinary Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
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Miyoshi Seiji
Faculty of Engineering Science, Kansai University, 3-3-35 Yamatecho, Suita, Osaka 564-8680, Japan
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Miyoshi Seiji
Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita, Osaka 564-8680
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Okada Masato
Division of Allergy and Rheumatology, St. Luke's International Hospital, Japan
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