系統識別號 | U0002-2209202012083300 |
---|---|
DOI | 10.6846/TKU.2020.00659 |
論文名稱(中文) | 使用對抗生成網路去除影像動態模糊 |
論文名稱(英文) | Motion Deblur Using Generative Adversarial Network |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊工程學系資訊網路與多媒體碩士班 |
系所名稱(英文) | Master's Program in Networking and Multimedia, Department of Computer Science and Information Engine |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 108 |
學期 | 2 |
出版年 | 109 |
研究生(中文) | 徐旨暘 |
研究生(英文) | Jr-Yang Shiu |
學號 | 606420106 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | 英文 |
口試日期 | 2020-07-07 |
論文頁數 | 45頁 |
口試委員 |
指導教授
-
顏淑惠
共同指導教授 - 林慧珍 委員 - 林慧珍 委員 - 顏淑惠 委員 - 凃瀞珽 |
關鍵字(中) |
對抗生成網路 影像去模糊 |
關鍵字(英) |
Deblurring Generative Adversarial Network |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
本篇論文以對抗生成網路作為基本架構,訓練目標為取得模糊影像和清晰影像間的差異,利用結果將模糊影像重建為清晰影像來達到去模糊的效果。所提出的方法架構中使用了ResNet[4]中的residual block堆疊將訓練的重點放在模糊和清晰影像差異上,並使用Atrous Spatial Pyramid Pooling (ASPP) [13]的方法來加強高頻特徵的傳遞。而在對抗生成的部分使用兩個鑑別器針對不同目標做判斷,加強影像細部還原同時還要保持影像整體的完整性,最後在計算loss時加入計算從模糊影像所取出的邊界影像和清晰影像的邊界影像的差異,來針對影像邊界作為主要目標。 |
英文摘要 |
In this paper, we use a generative adversarial network (GAN) as the basic structure to solve the motion blurring problem. Short cuts and residual blocks are adopted so the learning focuses on the difference between the blurred image and the clear image. Atrous Spatial Pyramid Pooling (ASPP) [13] method is used to enlarge receptive fields while preserving the high-frequency features. In the part of the discriminator, global and local discriminators are used to strengthen the restoration of image details as well as to maintain the integrity of the image. Finally, the edge loss is used to insure the restored image can preserve the structure details as the clear one. Test on two public data sets, Kohler and Gopro test sets, our method shows a good result on Kohler but not as good on Gopro. |
第三語言摘要 | |
論文目次 |
目錄 第一章 緒論 1 第二章 相關文獻回顧 4 第三章 研究方法 7 3.1 對抗生成網路 7 3.2 方法架構 9 3.2.1 Discriminator 9 3.2.2 Generator 11 3.3 損失函數 18 3.4 訓練數據 22 3.5 訓練流程 24 第四章 實驗 25 4.1 Kohler Dataset 25 4.2 Gopro dataset 26 4.3 各元素比較 27 4.4 各方法比較 29 第五章 結論 32 參考文獻 33 附錄:英文論文 35 圖目錄 圖表 1. Uniform & non-uniform motion blur images (a) & (c) 分別為Uniform & non-uniform motion模糊影像,而(b) & (d)則是以本文方法去除模糊後的結果。 2 圖表 2. generative adversarial network基本架構 8 圖表 3. 本文方法基本架構 9 圖表 4. Discriminator架構,數字為channel number,N 是patches的數目. 10 圖表 5. Generator架構,數字為channel number 11 圖表 6. residual block,截圖自Deep Residual Learning for Image Recognition [4] 12 圖表 7. dilated convolution,截圖自Atrous Spatial Pyramid Pooling [13] 13 圖表 8. ASPP,截圖自Atrous Spatial Pyramid Pooling [13] 14 表目錄 表格 1. Generator參數設定 (input is 256×256×3) 16 表格 2. Global discriminator參數設定 17 表格 3. Local discriminator參數設定 17 |
參考文獻 |
[1] S. Nah, T. Hyun, K. Kyoung, and M. Lee. Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring. 2016. [2] Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiˇr´ı Matas. Deblurgan: Blind motion deblurring using conditional adversarial networks. 2017. [3] Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. 2018. [4] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. 2015. [5] C. Li and M. Wand. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. 2014. [6] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville. Improved Training of Wasserstein GANs. 2017. [7] M. Mathieu, C. Couprie, and Y. LeCun. Deep multi-scale video prediction beyond mean square error. ICLR, 2016. [8] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection. [9] Zheng, S., Zhu, Z., Cheng, J., Guo, Y., & Zhao, Y. Edge Heuristic GAN for Non-Uniform Blind Deblurring. IEEE2019. [10] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and MingHsuan Yang. Blind image deblurring using dark channel prior. In Proceedings of the IEEE 2016. [11] A. Chakrabarti. A neural approach to blind motion deblurring. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. [12] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks. arxiv, 2016. [13] L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv:1606.00915, 2016 [14] F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in ICLR, 2016. [15] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI, 2015. [16] A. Jolicoeur-Martineau. ‘‘The relativistic discriminator: A key element missing from standard GAN.’’ Jul. 2018. [17] J. Sun, W. Cao, Z. Xu, and J. Ponce. Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal. 2015. [18] L. Xu, S. Zheng, and J. Jia. Unnatural L0 Sparse Representation for Natural Image Deblurring. 2013. [19] DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better ICCV 2019 |
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