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Bayesian Image Restoration for Poisson Corrupted Image Using a Latent Variational Method with Gaussian MRF
https://ipsj.ixsq.nii.ac.jp/records/141581
https://ipsj.ixsq.nii.ac.jp/records/141581b4571b25-b567-4e0c-9239-e8f07d375dde
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-TOM0801008.pdf (1.1 MB)
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Copyright (c) 2015 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Trans(1) | |||||||
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| 公開日 | 2015-03-30 | |||||||
| タイトル | ||||||||
| タイトル | Bayesian Image Restoration for Poisson Corrupted Image Using a Latent Variational Method with Gaussian MRF | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | Bayesian Image Restoration for Poisson Corrupted Image Using a Latent Variational Method with Gaussian MRF | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | [オリジナル論文] Poisson corrupted image, Bayesian inference, image restoration | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||
| 資源タイプ | journal article | |||||||
| 著者所属 | ||||||||
| University of Electro-Communications | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| University of Electro-Communications | ||||||||
| 著者名 |
Hayaru, Shouno
× Hayaru, Shouno
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| 著者名(英) |
Hayaru, Shouno
× Hayaru, Shouno
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| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We treat an image restoration problem with a Poisson noise channel using a Bayesian framework. The Poisson randomness might be appeared in observation of low contrast object in the field of imaging. The noise observation is often hard to treat in a theoretical analysis. In our formulation, we interpret the observation through the Poisson noise channel as a likelihood, and evaluate the bound of it with a Gaussian function using a latent variable method. We then introduce a Gaussian Markov random field (GMRF) as the prior for the Bayesian approach, and derive the posterior as a Gaussian distribution. The latent parameters in the likelihood and the hyperparameter in the GMRF prior could be treated as hidden parameters, so that, we propose an algorithm to infer them in the expectation maximization (EM) framework using loopy belief propagation (LBP). We confirm the ability of our algorithm in the computer simulation, and compare it with the results of other image restoration frameworks. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | We treat an image restoration problem with a Poisson noise channel using a Bayesian framework. The Poisson randomness might be appeared in observation of low contrast object in the field of imaging. The noise observation is often hard to treat in a theoretical analysis. In our formulation, we interpret the observation through the Poisson noise channel as a likelihood, and evaluate the bound of it with a Gaussian function using a latent variable method. We then introduce a Gaussian Markov random field (GMRF) as the prior for the Bayesian approach, and derive the posterior as a Gaussian distribution. The latent parameters in the likelihood and the hyperparameter in the GMRF prior could be treated as hidden parameters, so that, we propose an algorithm to infer them in the expectation maximization (EM) framework using loopy belief propagation (LBP). We confirm the ability of our algorithm in the computer simulation, and compare it with the results of other image restoration frameworks. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AA11464803 | |||||||
| 書誌情報 |
情報処理学会論文誌数理モデル化と応用（TOM） 巻 8, 号 1, p. 62-71, 発行日 2015-03-30 |
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| ISSN | ||||||||
| 収録物識別子タイプ | ISSN | |||||||
| 収録物識別子 | 1882-7780 | |||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
