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A Study of Effective Algorithm for Image-Based Color Ink Diffusion Rendering
|關鍵字:||Chinese ink;中國水墨;ink diffusion;image-based;painterly rendering;nonphotorealistic rendering;Kubelka-Munk theory;水墨渲染;以影像為基礎;繪圖風格成圖;非擬真成圖;庫貝卡-蒙克理論||出版社:||資訊科學系所||引用:||[BTS2006] A. Bousseau, M. Kaplan, J. Thollot, and F. X. Sillion, “Interactive watercolor rendering with temporal coherence and abstraction,” Proceedings of the 4th international Symposium on Non-Photorealistic Animation and Rendering (NPAR ''06), pp. 141-149, 2006. [CA2002] C. Chan and E. Akleman, “Two Methods for Creating Chinese Painting,” Proceedings of 10th Pacific Conference on Computer Graphics and Applications (PG'02), pp. 403-412, 2002. [CAS+1997] C. J. Curtis, S. E. Anderson, J. E. Seims, K. W. Fleischery, and D. H. Salesin, “Computer-Generated Watercolor,” Proceedings of 24th Annual Conference on Computer Graphics & Interactive Techniques, ACM SIGGRAPH, ACM Press, pp. 421-430, 1997. [CL2006] M. T. Chi and T. Y. 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Ouhyoung, “On the Effects of Haptic Display in Brush and Ink Simulation for Chinese Painting and Calligraphy,” Proceedings of 10th Pacific Conference on Computer Graphics and Applications (PG'02), pp. 439-441, 2002. [YLP2003] J. H. Yu, G. M. Luo, and Q. S. Peng, “Image-based synthesis of Chinese landscape painting,” Journal of Computer Science and Technology, Vol. 18, No. 1, pp. 22-28, 2003. [YO2002] J. W. Yeh and M. Ouhyoung, “Non-Photorealistic Rendering in Chinese Painting of Animals,” Journal of System Simulation, Vol. 14, No. 6, pp. 1220-1224, 2002. [ZST+1999] Q. Zhang, Y. Sato, J. Y. Takahashi, K. Muraoka, and N. Chiba, “Simple Cellular Automaton-Based Simulation of Ink Behaviour and Its Application to Suibokuga-Like 3D Rendering of Trees,” The Journal of Visualization and Computer Animation, Vol. 10, pp. 27-37, 1999.||摘要:||
在計算機圖學（computer graphics）的範疇中，中國水墨畫（Chinese in painting）的模擬是非擬真成圖（non-photorealistic rendering, NPR）領域裏重要的研究主題之一。本論文提出了兩個以影像為基礎（image-based）、非筆觸的（non-stroke）成圖演算法，來將一張輸入影像自動合成為一張具有彩色水墨擴散（color ink diffusion）、溼中溼水拓效果（wet-in-wet flow effect）、以及黑邊效果（dark-edge effect）－－三種常見於水墨畫效果的繪圖風格影像。首先，我們提出一個以影像為基礎的彩色水墨成圖演算法（IBCIDR）來模擬彩色水墨擴散風格。接著，在IBCIDR的基礎之上，我們提出另一個有效的以影像為基礎的彩色水墨擴散合成演算法（EIBCIDS）來產生更好的彩色水墨擴散、溼中溼水拓（wet-in-wet flow effect）、以及黑邊效果。
以影像為基礎的彩色水墨成圖演算法（IBCIDR）包含三個主要的階段：特徵提取階段、混色階段、以及彩色水墨擴散合成階段。在特徵提取階段，利用亮度分段及色彩分割（luminance division and color segmentation, LDCS）來將參考影像的資訊抽象化；在混色階段，庫貝卡－蒙克理論（Kubelka-Munk theory, K-M theory）用來計算兩種顏料重疊時的混色結果；接著，在彩色水墨擴散合成階段，我們提出一個以自然法則為基礎的（physically-based）彩色水墨擴散模型，來模擬彩色水墨滴在一張用材質合成（texture synthesis）技術所產生的吸水宣紙上的擴散現象。我們的IBCIDR演算法不僅改進了傳統水墨擴散模擬只侷限在黑白水墨的缺點，同時也展示了一種不需要建構任何筆觸，將彩色影像自動轉換成令人激賞的水墨擴散風格影像的方法。
Chinese ink painting simulation is an important research area of non-photorealistic rendering (NPR) in the computer graphics community. This thesis proposes two image-based, non-stroke, painterly rendering algorithms for automatically synthesizing an image with color ink diffusion, wet-in-wet flow effect, and dark-edge effect -- three famous effects existing in the Chinese ink painting. We first present an image-based color ink diffusion rendering (IBCIDR) algorithm for the simulation of color ink diffusion. Based on this IBCIDR, we next suggest an effective image-based color ink diffusion synthesis (EIBCIDS) algorithm to mimic better diffusion, wet-in-wet flow, and dark-edge effects.
The image-based color ink diffusion rendering (IBCIDR) algorithm contains three main phases. In the feature extraction phase, the information of the reference image is simplified by luminance division and color segmentation (LDCS). In the color mixing phase, the Kubelka-Munk (K-M) theory is employed to approximate the result when one pigment is painted upon another pigment layer. Then in the color ink diffusion synthesis phase, the physically-based color ink diffusion model (CIDM) that we propose is employed to simulate the result of color ink diffusion in absorbent paper using a texture synthesis technique. Our IBCIDR algorithm eliminates the drawback of conventional Chinese ink simulations, which are limited to the black ink domain, and our approach demonstrates that, without using any strokes, a color image can be automatically converted to the diffused ink style with a visually pleasing appearance.
The IBCIDR is the first, non-stroke, image-based color ink diffusion rendering algorithm. However, the IBCIDR also showed four drawbacks-- “color shifting,” “detail missing,” no wet-in-wet flow effect, and no dark-edge effect. Consequently, we propose a new, effective, non-stroke, image-based color ink diffusion synthesis algorithm (called EIBCIDS) to overcome these four problems.
The EIBCIDS contains three main stages: effective color ink diffusion synthesis (ECIDS), controllable SK-M edge blending, and controllable wet-in-wet flow technique (CWFT). In the ECIDS algorithm, we propose a new K-M equation (SK-M) to properly mix pigment color. We also present a new color ink diffusion synthesis algorithm which uses a new overlapping equation (NOL) to properly overlap the deposit layer with a diffusion layer. Thus, the diffused image can show more similar tone and more ink diffusion details than IBCIDR. In addition, a new image abstraction approach is suggested to generate a smoother abstraction from the reference image. In the controllable SK-M edge blending algorithm, the suitable edges are extracted from the reference image by edge extraction approach. By employing the SK-M equation, the edge map created by edge extraction is blended with the diffused image which is simulated by ECIDS. The user can control the detail and strength of dark-edge effect by using an edge threshold coefficient and an edge strength coefficient. The controllable wet-in-wet flow technique (CWFT) is suggested, independent of the ink diffusion algorithm to decrease the complexity of the entire simulation system. In particular, we use the adaptive length line integral convolution (ALLIC) to represent the global flow of the reference image. Referring to the luminance of the reference image and the desired weight coefficient provided by the user, a controllable flow map is constructed. Finally, we employ our SK-M equation again to blend the controllable flow map with the diffused image to generate the final rendered result. Our EIBCIDS algorithm has four advantages: visually plausible, controllable, independent, and simple.
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