Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures 8

• Material Type: 博士論文
• ISSN: 1099-4300
• Title: Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures
• Volume: 23 8
• 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
• Degree grantor/type: 徳島大学
• Date Granted: 2022-03-23
• Date Granted (W3CDTF): 2022-03-23
• Dissertation Number: 甲第3606号
• Degree Type: 博士（保健学）
• Conferring No. (Dissertation): 甲第3606号
• 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.
• DOI: info:doi/10.3390/e23081005; https://doi.org/info:doi/10.3390/e23081005
• Persistent ID (NDL): info:ndljp/pid/12263929; https://dl.ndl.go.jp/pid/12263929
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2022-05-09T11:57:37+09:00
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
• Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
• Availability of remote photoduplication service: 可
• Periodical Title (URI): http://repo.lib.tokushima-u.ac.jp/116149
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp