政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150174
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 110934/141859 (78%)
造访人次 : 47683969      在线人数 : 973
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/150174


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/150174


    题名: 以山水畫為主的水墨風格化生成對抗網路
    Generative Adversarial Network for Landscape Ink Wash Painting Style
    作者: 詹彬
    Zhan, Bin
    贡献者: 紀明德
    Chi,Ming-Te
    詹彬
    Zhan,Bin
    关键词: 水墨畫
    山水畫
    風格遷移
    生成對抗網路
    多模態化
    Chinese Ink Wash Painting,
    Landscape Painting
    Style Transfer
    Generative Adversarial Networks
    Multimodal
    日期: 2024
    上传时间: 2024-03-01 13:42:52 (UTC+8)
    摘要: 這項研究旨在提升生成對抗網路(GAN)在水墨畫生成領域的表現。我們引入了深度值的概念,並設計了三種損失函數:Recognize Loss、Geometric Loss和TV Loss,以符合水墨畫生成的需求。深度值的引入旨在模擬水墨畫中的墨色濃度和深度。這些損失函數的作用不僅僅是提高生成圖像的辨識性,更進一步地,它們有助於捕捉水墨畫的獨特特徵,如筆觸的流暢性、線條的自然性以及紋理的豐富性。通過保持幾何形狀和結構的一致性,我們可以確保生成的畫作在整體布局和結構上與原始畫作保持一致,進而增強其藝術性和真實感。此外,抑制噪聲和細節的損失函數有助於消除生成圖像中的不必要細節。我們期望這些改進能夠提高生成圖像的藝術性,並增強其與傳統水墨畫的相似度。
    This research aims to enhance the performance of Generative Adversarial Networks (GANs) in the field of Chinese ink painting generation. To achieve this goal, we introduce the concept of depth values and design three types of loss functions tailored to the requirements of ink painting generation. The introduction of depth values is primarily aimed at simulating the ink density and depth in Chinese ink paintings. Through the application of these new loss functions, we aim to increase the artistic quality of the generated images while enhancing their similarity to traditional Chinese ink paintings.
    參考文獻: 參考文獻
    [1] M. Ashikhmin, “Synthesizing natural textures,” in ACM Symposium on Interactive 3D Graphics and Games, 2001.
    [2] L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,”ArXiv, vol. abs/1508.06576, 2015.
    [3] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Computer Vision (ICCV),2017 IEEE International Conference on, 2017.
    [4] B. Li, C. Xiong, T. Wu, Y. Zhou, L. Zhang, and R. Chu, “Neural abstract styletransfer for chinese traditional painting,” 2018.
    [5] A. Xue, “End-to-end chinese landscape painting creation using generative adversarial networks,” 2020.
    [6] S. Luo, S. Liu, J. Han, and T. Guo, “Multimodal fusion for traditional chinesepainting generation,” in Pacific Rim Conference on Multimedia, 2018.
    [7] B. He, F. Gao, D. Ma, B. Shi, and L.-Y. Duan, “Chipgan: A generative adversarialnetwork for chinese ink wash painting style transfer,” in Proceedings of the 26thACM international conference on Multimedia, 2018, pp. 1172–1180.
    [8] A. A. Efros and T. K. Leung, “Texture synthesis by non-parametric sampling,”
    Proceedings of the Seventh IEEE International Conference on Computer Vision,vol. 2, pp. 1033–1038 vol.2, 1999.
    [9] A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. Salesin, “Image analogies,” Proceedings of the 28th annual conference on Computer graphics andinteractive techniques, 2001.
    [10] V. Dumoulin, J. Shlens, and M. Kudlur, “A learned representation for artisticstyle,” ArXiv, vol. abs/1610.07629, 2016.
    [11] X. Huang and S. J. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” 2017 IEEE International Conference on Computer
    Vision (ICCV), pp. 1510–1519, 2017.
    [12] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in NIPS, 2014.
    [13] T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “Highresolution image synthesis and semantic manipulation with conditional gans,”
    2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.8798–8807, 2017.
    [14] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEEinternational conference on computer vision, 2017, pp. 2223–2232.
    [15] H. Zhang, I. J. Goodfellow, D. N. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” ArXiv, vol. abs/1805.08318, 2018.
    [16] D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single imageusing a multi-scale deep network,” 2014.
    [17] C. Godard, O. M. Aodha, and G. J. Brostow, “Unsupervised monocular depthestimation with left-right consistency,” 2017.
    [18] C. Chan, F. Durand, and P. Isola, “Learning to generate line drawings that conveygeometry and semantics,” 2022.
    [19] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robustmonocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” 2020.
    [20] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking theinception architecture for computer vision,” 2015.
    [21] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry,A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” 2021.
    [22] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment:from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
    [23] Q. Huynh-Thu, “Scope of validity of psnr in image/video quality assessment,”Electronics Letters, vol. 44, pp. 800–801(1), June 2008. [Online]. Available:https://digital-library.theiet.org/content/journals/10.1049/el_20080522
    描述: 碩士
    國立政治大學
    資訊科學系
    110753213
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110753213
    数据类型: thesis
    显示于类别:[資訊科學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    321301.pdf35862KbAdobe PDF2检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈