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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79512
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dc.contributor.advisor陳炳宇(Bing-Yu Chen)
dc.contributor.authorLi-Yu Chenen
dc.contributor.author陳力宇zh_TW
dc.date.accessioned2022-11-23T09:02:21Z-
dc.date.available2021-11-08
dc.date.available2022-11-23T09:02:21Z-
dc.date.copyright2021-11-08
dc.date.issued2021
dc.date.submitted2021-10-04
dc.identifier.citationR. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S¨usstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2274–2282, 2012. R. Bellini, Y. Kleiman, and D. Cohen-Or. Time-varying weathering in texture space. ACM Trans. Graph., 35(4), July 2016. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. U. Demir and G. Unal. Patch-based image inpainting with generative adversarial networks, 2018. J. Dorsey, H. K. Pederseny, and P. Hanrahan. Flow and changes in appearance. In ACM SIGGRAPH 2006 Courses, SIGGRAPH ’06, page 3–es, New York, NY, USA, 2006. Association for Computing Machinery. A. A. Efros and W. T. Freeman. Image quilting for texture synthesis and transfer. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01, page 341–346, New York, NY, USA, 2001. Association for Computing Machinery. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 27. Curran Associates, Inc., 2014. S. Iizuka, Y. Endo, Y. Kanamori, and J. Mitani. Single image weathering via exemplar propagation. In Proceedings of the 37th Annual Conference of the European Association for Computer Graphics, EG ’16, page 501–509, Goslar, DEU, 2016. Eurographics Association. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5967–5976, 2017. J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and superresolution. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, Computer Vision – ECCV 2016, pages 694–711, Cham, 2016. Springer International Publishing. L. Karacan, Z. Akata, A. Erdem, and E. Erdem. Learning to generate images of outdoor scenes from attributes and semantic layouts. CoRR, abs/1612.00215, 2016. C. Ledig, L. Theis, F. Husz´ar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-realistic single image super-resolution using a generative adversarial network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 105–114, 2017. J. Lu, A. S. Georghiades, A. Glaser, H.Wu, L.-Y.Wei, B. Guo, J. Dorsey, and H. Rushmeier. Context-aware textures. ACM Trans. Graph., 26(1):3–es, Jan. 2007. M. Mirza and S. Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014. A. Odena, C. Olah, and J. Shlens. Conditional image synthesis with auxiliary classifier gans. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML’17, page 2642–2651. JMLR.org, 2017. D. Pathak, P. Kr¨ahenb¨uhl, J. Donahue, T. Darrell, and A. A. Efros. Context encoders: Feature learning by inpainting. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2536–2544, 2016. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In Y. Bengio and Y. LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham, 2015. Springer International Publishing. T.-C.Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro. High-resolution image synthesis and semantic manipulation with conditional gans. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8798–8807, 2018. S. Xue, J. Dorsey, and H. Rushmeier. Stone weathering in a photograph. Computer Graphics Forum, 30(4):1189–1196, 2011.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79512-
dc.description.abstract風化作用是個常見的自然現象,自然界中物質的老化無時無刻地在發生,同時也會對表面造成很大的影響。這表示就算是同一種物質本身,在視覺上也會有許多不一樣的風化效果。然而,在一張材質圖像中,擁有風化效果的位置或是程度不一定能滿足使用者的需求,若是存在可以讓使用者隨意調整風化效果的工具將會帶來很大的幫助。 在本論文中,我們展示了一個導引圖像中風化效果生成的方法,讓使用者可以生成風化過程的圖像。我們的核心方法可以分為三個階段,首先輸入的材質圖像會先被分析來得到不同像素風化程度的對應表 (age map),接著我們拿這張對應表當作條件訓練一個圖像對圖像轉換的深度學習模型。訓練完成後,透過改變風化程度的對應表,例如自動內插或是使用者手動修改,便可以將對應表當作條件輸入深度學習模型來得到新的風化材質圖像。zh_TW
dc.description.provenanceMade available in DSpace on 2022-11-23T09:02:21Z (GMT). No. of bitstreams: 1
U0001-0110202111275400.pdf: 11149176 bytes, checksum: 9eef5849c70180b4f37b209269f1247f (MD5)
Previous issue date: 2021
en
dc.description.tableofcontents口試委員會審定書 i 致謝 ii 中文摘要 iii Abstract iv List of Figures vii List of Tables xii Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Physically-based Simulation 4 2.2 Time-varying Weathering 5 2.3 Generative Adversarial Network 6 2.4 Image-to-image Translation 7 Chapter 3 Method 9 3.1 Age Map 10 3.1.1 Age Map by previous work 10 3.1.2 Age Map by SLIC algorithm 11 3.2 Image-to-image Translation Network 13 3.2.1 Network Architecture 13 3.2.2 Objective Function 14 3.3 Texture Synthesis 15 Chapter 4 Applications 18 4.1 De-weathering Process 19 4.2 Weathering Process 20 4.3 Delta Map Blending 22 Chapter 5 Experiments 24 5.1 Performance 24 5.2 Generative Adversarial Network Training 26 5.3 Ablation Study 28 5.3.1 Age Map Patch Size 28 5.3.2 Perceptual Loss 29 5.4 Results 31 5.4.1 User Modification 32 5.4.2 De-weathering Process 33 5.4.3 Weathering Process 35 Chapter 6 Limitations and Future Work 37 Chapter 7 Conclusion 39 Bibliography 41
dc.language.isoen
dc.title使用圖像對圖像轉換導引風化材質生成zh_TW
dc.titleGuided Image Weathering using Image-to-Image Translationen
dc.date.schoolyear109-2
dc.description.degree碩士
dc.contributor.oralexamcommittee莊永裕(Hsin-Tsai Liu),王鈺強(Chih-Yang Tseng)
dc.subject.keyword圖像風化生成,圖像對圖像轉換,zh_TW
dc.subject.keywordimage weathering,image-to-image translation,en
dc.relation.page43
dc.identifier.doi10.6342/NTU202103491
dc.rights.note同意授權(全球公開)
dc.date.accepted2021-10-05
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
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