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研究生: 王宇捷
Wang, Yu-Chieh
論文名稱: 基於空間引導機制之高頻細節增強的影像運動模糊去除
HGANet: High-frequency information Guided Augmentation Network for Image Motion Deblurring
指導教授: 葉家宏
Yeh, Chia-Hung
口試委員: 林青嶔
Lin, Ching-Chin
張傳育
Chang, Chuan-Yu
郭鐘榮
Kuo, Chung-Jung
葉家宏
Yeh, Chia-Hung
口試日期: 2022/12/28
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 28
中文關鍵詞: 影像去模糊非均勻運動模糊去除圖像恢復高頻資訊
英文關鍵詞: Image Deblurring, Non-uniform motion deblurring, Image Restoration, High-frequency information
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202300073
論文種類: 學術論文
相關次數: 點閱:99下載:12
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  • 在影像運動去模糊中,多階段架構已被廣為使用且獲得卓越的效能。而過去傳統方法通常透過提取模糊輸入影像的空間細節來修復退化的影像,但由於輸入模糊圖像無法提供準確的高頻細節,因此降低了整體去模糊演算法的性能。為了解決這個問題,本文提出一種新的雙階段架構,該架構可以通過提取模糊圖像的高頻細節資訊來重建詳細的紋理。它使用了一種有監督的引導機制來提供精確的空間細節來重新校準多尺度特徵。此外,該方法還設計一個基於注意力的特徵聚合器,可以自適應地融合來自不同階段的有影響的特徵,以抑制傳遞到下一個階段的冗餘資訊。本文通過實驗證明,所提出的法在 GoPro 和 HIDE 基準數據集上的去模糊性能和計算複雜度方面都優於其他現有方法。

    Multi-stage image processing architectures have been widely used for deblurring and have shown good results. Traditional methods try to restore the details of the blurred image by using the information in the blurred image itself, but this can be problematic because the blurred image may not contain enough high-frequency detail, leading to a loss of quality in the deblurred image. To solve this problem, we propose a dual-stage network that is able to retrieve high-frequency detail from the blurred image and use it to restore the image's texture in more detail. We also introduce a mechanism that uses known image content to better calibrate contextual information, and an attention-based feature aggregator that adaptively combines influential features from different stages to eliminate unnecessary information and improve the efficiency of the multi-stage architecture. Our experiments on GoPro and HIDE datasets show that our network performs better and is more efficient than existing methods.

    中文摘要 i Abstract ii Content iii List of Tables iv List of Figures v Chapter 1 Introduction 1 1.1 Research motivation & background 1 1.2 Research purpose & overview 2 1.3 Thesis architecture 5 Chapter 2 Related Work 6 2.1 Single-stage Architecture 6 2.2 Multi-stage Architecture 8 Chapter 3 Methodology 10 3.1 Network Architecture 10 3.2 High-frequency perception Enhancement Module 13 3.3 Multi-stage Selection Module 14 Chapter 4 Experimental Results 16 4.1 Dataset and implementation details 16 4.2 Performance Comparisions 17 4.3 Ablation Study 20 Chapter 5 Conclusion 22 References 23

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