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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁肇隆 | zh_TW |
dc.contributor.advisor | Chao-Lung Ting | en |
dc.contributor.author | 周楷諭 | zh_TW |
dc.contributor.author | Kai-Yu Chou | en |
dc.date.accessioned | 2023-01-10T17:05:49Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-01-07 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-12-30 | - |
dc.identifier.citation | D Glasner, S Bagon, M Irani. “Super-resolution from a single image”[C]. IEEE Conference on Computer Vision, 2009. 349-356
J. L. Harris (1964). Diffraction and Resolving Power. J. O. S. A.,54(7) , 931-936 J. W. Goodman (1968). Introdution to Fourier Optics. FD York: McGraw-Hill R. Y. Tsai and T. S. Huang. Multipleframe image restoration and registration. In Advances in Computer Vision and Image Processing, pages 317–339, 1984. 1 F. M. Candocia and J. C. Principe, “Super-resolution of images based on local correlations,” IEEE Transactions on Neural Network, vol. 10, no. 2, pp. 372–380, 1999. C. C. Wei and C. H. Chen, “Generalized Bilinear Interpolation of Motion Vectors for Quad-Tree Mesh,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing 15-17 pp.635-638 Aug. 2008. H Ur, D Gross. Improved resolution from sub-pixel shifted pictures[J]. CVGIP: Graphical Models and Image Processing, 1992, 54(2): 181-186. M. Irani and S. Peleg, “Improving resolution by image registration,” CVGIP: Graphical models and image processing, vol. 53, no. 3, pp. 231–239, 1991. B C Tom, A k Katsaggelos. (1996). Resolution enhancemant of video sequences using motion compensation. In:Proc IEEE image Processing,1:713-716 He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” Computer Vision-ECCV, pp. 184-199, 2014 Li, J., et al., “A Frequency Domain Neural Network for Fast Image Super-resolution.”International Joint Conference on Neural Networks (IJCNN), 2018 Xue, S., et al., “Faster Image Super-Resolution by Improved Frequency Domain Neural Networks.” Signal, Image and Video Processing, submitted, 2019 Dario, Fuoli, et al., “Fourier Space Losses for Efficient Perceptual Image Super-Resolution.” Computer Vision-ICCV, 2021 Runyuan Cai, et al., “FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform.” Computer Vision and Pattern Recognition, 2021 Kim, Jiwon, et al., “Accurate Image Super-Resolution Using Very Deep Convolutional Networks.” Computer Vision and Pattern Recognition, 2015 Cai Jianyu, et al., “Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs.”ACL, 2021 C. Dong, et al., “Accelerating the Super-Resolution Convolutional Neural Network,” Computer Vision-ECCV, 2016 Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4681-4690. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 770- 778. https://commons.wikimedia.org/wiki/File:HSV_color_solid_cylinder.png Gong R, Wang Y, Cai T, Shao X. How to deal with color in super resolution reconstruction of images. Opt Exp. 2017 May 15;25(10):11144–11156. https://cvnote.ddlee.cc/2019/09/22/image-super-resolution-datasets | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83168 | - |
dc.description.abstract | 近年來,隨著數位顯示器的進步,過去製作的數位內容已無法與之匹配,因此運用深度學習方法,使影像解析度增強的議題,變得廣為人們所研討。本論文以對抗網路為基礎,針對圖像超解析度(Super Resolution)進行研究,不同於過往直接於空間域(Spatial Domain)對影像進行解析度增強之方式,而是透過傅立葉轉換將影像由空間域轉換至頻率域(Frequency Domain),將所得之影像頻譜圖乘上權重矩陣,利用深度網路學習影像中的高頻資訊,最終完成對高解析度影像的重建。實驗中將影像分為無字幕影像及帶有字幕之影像,並對其解析度進行三種倍率的增強實驗。據實驗結果顯示,對於無字幕影像進行三種倍率的增強時,與傳統的影像插值法及SRGAN進行比較,本論文所提出之方法在PSNR及SSIM的比較上是優於另外兩種方法。而對於帶有字幕之影像進行解析度增強,將字幕從影像中擷取出來,分別對其進行解析度增強,從實驗結果可知本研究所提出之方法,亦是優於影像插值法及SRGAN。 | zh_TW |
dc.description.abstract | In recent years, with the development of digital displays, the digital content produced in the past can’t match with it. Therefore, the issue of image resolution enhancement using deep learning methods has become widely discussed. In this paper, we study the super resolution of images based on the adversarial network. Instead of directly enhancing the image resolution in the spatial domain in the past, we convert the image from the spatial domain to the frequency domain by Fourier transform, then multiplies the result image spectrum map by the weight matrix, and use the Deep learning to learn the high-frequency information in the image, consequently the high resolution image reconstruction will be completed. In the experiment, the images were divided into two sections: images without subtitles and images with subtitles, and the resolution was enhanced at three different magnifications. The experimental results showed that the proposed method is superior to the traditional image interpolation method and SRGAN in terms of PSNR and SSIM when compare the three enhancement rates with the other two methods. For the resolution enhancement of images with subtitles, the subtitles are extracted from the images , after that the resolution is enhancement respectively. From the resolution enhancement of images with subtitles as we know, the method proposed in this study is better than the image interpolation method and SRGAN. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-10T17:05:49Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-01-10T17:05:49Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員審定書 #
誌謝 i 摘要 ii ABSTRACT iii 目 錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 論文架構 2 第二章 文獻回顧 3 2.1 解析度增強於空間域 3 2.1.1 基於插值方法 3 2.1.2 基於重建的方法 8 2.1.3 基於學習的方法 10 2.2 解析度增強於頻率域 12 2.3 對抗網路於解析度增強 16 2.3.1 深度學習於解析度增強 16 2.3.2 圖像色彩 19 第三章 研究方法 22 3.1 實驗流程 22 3.2 影像處理及網路架構 23 3.2.1 字幕分離之影像處理 23 3.2.2 訓練方法 30 3.3 損失函數及評斷標準 34 3.3.1 損失函數 34 3.3.2 評斷標準 35 第四章 實驗結果與討論 39 4.1 實驗環境及資料集 39 4.2 無字幕影像解析度增強之實驗結果 40 4.3 顏色偏移 49 4.4 字幕圖像 51 第五章 結論 55 參考文獻 56 | - |
dc.language.iso | zh_TW | - |
dc.title | 以對抗網路學習高解析度影像之高頻率成份來增強影像之解析度 | zh_TW |
dc.title | Resolution enhancement by using GAN to learning the high frequency components from high resolution images | en |
dc.title.alternative | Resolution enhancement by using GAN to learning the high frequency components from high resolution images | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 張恆華;張瑞益;王家輝 | zh_TW |
dc.contributor.oralexamcommittee | Herng-Hua Chang;Ray-I Chang;Jia-Hui Wang | en |
dc.subject.keyword | 圖像超解析度,深度學習,傅立葉轉換,生成對抗網路,影像處理, | zh_TW |
dc.subject.keyword | Image super-resolution,Deep learning,Fourier transform,Generative adversarial network,Image processing, | en |
dc.relation.page | 58 | - |
dc.identifier.doi | 10.6342/NTU202210180 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-01-03 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
顯示於系所單位: | 工程科學及海洋工程學系 |
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