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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150174


    Title: 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
    Authors: 詹彬
    Zhan, Bin
    Contributors: 紀明德
    Chi,Ming-Te
    詹彬
    Zhan,Bin
    Keywords: 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
    Date: 2024
    Issue Date: 2024-03-01 13:42:52 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    資訊科學系
    110753213
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753213
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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