Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

• Material Type: 博士論文
• Title: Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures
• Author/Editor: Kasai, Ryosuke; Yamaguchi, Yusaku; Kojima, Takeshi; Abou Al-Ola, Omar M.; Yoshinaga, Tetsuya
• Author Heading: Kasai, Ryosuke; Yamaguchi, Yusaku; Kojima, Takeshi; Abou Al-Ola, Omar M.; Yoshinaga, Tetsuya
• Publication, Distribution, etc.: MDPI
• Publication Date: 2021-07-31
• Publication Date (W3CDTF): 2021-07-31
• Alternative Title: 拡張指数型測度族の最小化に基づき導出された雑音に頑健な画像再構成
• Periodical title: Entropy
• No. or year of volume/issue: 23 8
• Volume: 23
• Issue: 8
• Pages: 1005-
• ISSN (Periodical Title): ISSN : 1099-4300
• Degree grantor/type: 徳島大学
• Date Granted: 2022-03-23
• Dissertation Number: 甲第3606号
• Degree Type: 博士（保健学）
• Text Language Code: eng
• Subject Heading: power-divergence measure; computed tomography; iterative reconstruction; maximum-likelihood expectation-maximization method; continuous-time image reconstruction
• Target Audience: 一般
• Note (General): The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide significant improvements over transform methods in computed tomography. In this paper, we present an extended class of power-divergence measures (PDMs), which includes a large set of distance and relative entropy measures, and propose an iterative reconstruction algorithm based on the extended PDM (EPDM) as an objective function for the optimization strategy. For this purpose, we introduce a system of nonlinear differential equations whose Lyapunov function is equivalent to the EPDM. Then, we derive an iterative formula by multiplicative discretization of the continuous-time system. Since the parameterized EPDM family includes the Kullback–Leibler divergence, the resulting iterative algorithm is a natural extension of the maximum-likelihood expectation-maximization (MLEM) method. We conducted image reconstruction experiments using noisy projection data and found that the proposed algorithm outperformed MLEM and could reconstruct high-quality images that were robust to measured noise by properly selecting parameters.
• Format (IMT): application/pdf
• Access Restrictions: インターネット公開
• Rights (production): This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
• Related Material (DOI): 10.3390/e23081005
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 徳島大学 : 徳島大学機関リポジトリ

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http://repo.lib.tokushima-u.ac.jp/files/public/11/116149/20220426163853612917/k3606_abstract.pdf fulltext

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