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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88835
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dc.contributor.advisor貝蘇章zh_TW
dc.contributor.advisorSoo-Chang Peien
dc.contributor.author張智堯zh_TW
dc.contributor.authorChih-Yao Changen
dc.date.accessioned2023-08-15T17:58:59Z-
dc.date.available2023-11-09-
dc.date.copyright2023-08-15-
dc.date.issued2023-
dc.date.submitted2023-07-11-
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[2] K. Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. Learning deep cnn denoiser prior for image restoration. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2808–2817, 2017.
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[4] K. Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte. Plug-and-play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:6360–6376, 2020.
[5] Yu Sun, Brendt Wohlberg, and Ulugbek S. Kamilov. An online plug-and-play al gorithm for regularized image reconstruction. IEEE Transactions on Computational Imaging, 5:395–408, 2018.
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[19] Yunjin Chen and Thomas Pock. Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1256–1272, 2017.
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[22] Tim Meinhardt, Michael Möller, Caner Hazirbas, and Daniel Cremers. Learning proximal operators: Using denoising networks for regularizing inverse imaging problems. 2017 IEEE International Conference on Computer Vision (ICCV), pages 1799–1808, 2017.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88835-
dc.description.abstract隨著圖像復原演算法的發展,許多人都同意先驗(prior)在提高復原性能方面扮演了很重要的角色。最近發展出的基於隨插即用 (Plug-and-Play) 的方法,將基於模型的圖像復原方法和基於學習的方法的優點相結合,因其高效能和易於實現而受到廣泛關注。
本論文探討基於隨插即用的圖像復原演算法,這是一種對於解決圖像復原問題來說很有效的方法。第一章介紹了本文的主題。第二章探討了將隨插即用先驗應用於成像問題中,包括建立圖像復原模型、圖像復原的最佳化問題、隨插即用架構以及相關工作。第三章涵蓋了基於可變分割演算法的隨插即用圖像復原,包括交替方向乘子法(ADMM)、半二次分割演算法(HQS)和加速迭代收縮/閾值演算法(FISTA),以及它們的收斂性分析和實驗結果。在第四章中,我們研究了與隨插即用相關的演算法,像是去噪正則化 (RED) 和多重代理共識平衡 (MACE),以及它們的收斂性分析和實驗結果。最後,第五章是本文的結論。總體而言,這篇論文全面介紹了基於隨插即用的圖像復原演算法和其相關的演算法以及它們在各種圖像復原任務中的應用。
zh_TW
dc.description.abstractWith the development of the image restoration algorithms, one can admit that priors play in a important role in improving restoration performance. The recently developed plug-and-play prior method, which incorporates the advantages of model-based image restoration method and the learning-based method, owes its popularity to its efficiency and the ease of implementation.
This paper focuses on Plug-and-Play based Image Restoration Algorithms, which are a promising and effective approach to address image restoration problems. Chapter 1 provides an introduction to the topic. In Chapter 2, the Plug-and-Play prior in imaging problem is discussed, including the modeling for Image Restoration, Optimization problem in Image Restoration, Plug-and-play framework, and related work. Chapter 3 covers Variable splitting algorithms for plug-and-play image restoration, including Alternating Direction Method of Multipliers (ADMM), Half Quadratic Splitting algorithm (HQS) and Fast Iterative Shrinkage/Thresholding Algorithm (FISTA), as well as their convergence analysis and experiment results. In Chapter 4, we also investigate PnP-related algorithms such as Regularization by Denoising (RED) and Multi-Agent Consensus Equilibrium (MACE), along with their convergence analysis and experiment results. Finally, Chapter 5 concludes the paper. Overall, this paper provides a comprehensive overview of plug-and-play based image restoration algorithms and Plug-and-Play related algorithms, as well as their applications in various image restoration tasks.
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dc.description.tableofcontentsAcknowledgements i
摘要 ii
Abstract iii
Contents v
List of Figures viii
List of Tables x
Chapter 1 Introduction 1
Chapter 2 Plug-and-Play Prior in Imaging Problem 3
2.1 Modeling of Image Restoration . . . . . . . . . . . . . . . . . . . . 3
2.2 Optimization Problem in Image Restoration . . . . . . . . . . . . . . 4
2.3 Plug-and-Play Framework . . . . . . . . . . . . . . . . . . . . . . . 6
Chapter 3 Variable Splitting Algorithms for Plug-and-play Image Restora tion 10
3.1 Alternating Direction Method of Multipliers . . . . . . . . . . . . . . 11
3.1.1 Introduction to PnP-ADMM . . . . . . . . . . . . . . . . . . . . . 11
3.1.2 Convergence Analysis of PnP-ADMM . . . . . . . . . . . . . . . . 12
3.1.3 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Half Quadratic Splitting Algorithm . . . . . . . . . . . . . . . . . . 17
3.2.1 Introduction to PnP-HQS . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2 Parameter Setting for Convergence . . . . . . . . . . . . . . . . . . 18
3.2.3 Comparison with PnP-ADMM . . . . . . . . . . . . . . . . . . . . 19
3.2.4 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.4.1 Image Deblurring . . . . . . . . . . . . . . . . . . . . 19
3.2.4.2 Single Image Super-Resolution . . . . . . . . . . . . . 22
3.3 Fast Iterative Shrinkage/Thresholding Algorithm . . . . . . . . . . . 25
3.3.1 Introduction to PnP-FISTA . . . . . . . . . . . . . . . . . . . . . . 25
3.3.2 Convergence Analysis of PnP-FISTA . . . . . . . . . . . . . . . . . 26
3.3.3 Comparison with PnP-ADMM . . . . . . . . . . . . . . . . . . . . 27
3.3.4 Online Variant of PnP-FISTA . . . . . . . . . . . . . . . . . . . . . 28
3.3.5 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Chapter 4 Plug-and-Play Related Algorithms 34
4.1 Regularization by Denoising . . . . . . . . . . . . . . . . . . . . . . 34
4.1.1 Introduction to RED . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1.2 Convergence Analysis of RED . . . . . . . . . . . . . . . . . . . . 36
4.1.3 Deep Image Prior Extension of RED . . . . . . . . . . . . . . . . . 37
4.1.3.1 Deep Image Prior . . . . . . . . . . . . . . . . . . . . 37
4.1.3.2 DeepRED . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.4 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Multi-Agent Consensus Equilibrium . . . . . . . . . . . . . . . . . . 43
4.2.1 Introduction to MACE . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Chapter 5 Conclusion 50
References 51
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dc.language.isoen-
dc.subject去噪正則化zh_TW
dc.subject多重代理共識平衡zh_TW
dc.subject隨插即用zh_TW
dc.subject影像復原zh_TW
dc.subject交替方向乘子法zh_TW
dc.subject半二次分割演算法zh_TW
dc.subject加速迭代收縮/閾值演算法zh_TW
dc.subjectPlug-and-Playen
dc.subjectFast Iterative Shrinkage/Thresholding Algorithmen
dc.subjectHalf Quadratic Splittingen
dc.subjectAlternating Direction Method of Multipliersen
dc.subjectImage Restorationen
dc.subjectRegularization by Denoisingen
dc.subjectMulti-Agent Consensus Equilibriumen
dc.title隨插即用方式的影像復原演算法zh_TW
dc.titlePlug-and-Play Based Image Restoration Algorithmsen
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee丁建均;鍾國亮;杭學鳴;曾建誠zh_TW
dc.contributor.oralexamcommitteeJian-Jiun Ding;Kuo-Liang Chung;Hsueh-Ming Hang;Chien-Cheng Tsengen
dc.subject.keyword隨插即用,影像復原,交替方向乘子法,半二次分割演算法,加速迭代收縮/閾值演算法,去噪正則化,多重代理共識平衡,zh_TW
dc.subject.keywordPlug-and-Play,Image Restoration,Alternating Direction Method of Multipliers,Half Quadratic Splitting,Fast Iterative Shrinkage/Thresholding Algorithm,Regularization by Denoising,Multi-Agent Consensus Equilibrium,en
dc.relation.page55-
dc.identifier.doi10.6342/NTU202301425-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2023-07-13-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept電信工程學研究所-
dc.date.embargo-lift2028-07-07-
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