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研究生:周宜瑩
研究生(外文):I-Ying Chou
論文名稱:基於貝茲曲線的影像修補演算法
論文名稱(外文):Image Inpainting Based on Bezier Curves
指導教授:吳俊霖吳俊霖引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊科學與工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:97
語文別:中文
論文頁數:55
中文關鍵詞:影像修補材質合成基於範例修補法邊緣結構重建貝茲曲線
外文關鍵詞:Image inpaintingTexture synthesisExemplar-based methodEdge structure reconstructionBezier curve
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影像修補是一種用來復原損壞影像、填補遺失區域的技術,其藉由影像周圍的已知資訊,達到修復損壞區域的目的。而修補完後的影像必須是完整、自然的,視覺上看起來就像是未損壞過的。目前的影像修補技術,在影像結構的修復上仍是一大瓶頸。為了能同時修補損壞區域的結構和材質,我們參考了A.Rares等人的基於邊緣影像修補演算法與Criminisi等人的基於範例影像修補演算法之作法,提出一個基於貝茲曲線的影像修補演算法。所提的方法是依賴待修補區域周圍的邊緣資訊來找出邊緣的所有配對,並使用貝茲曲線來描繪相配邊緣的連接曲線,藉此來改進結構銜接不好的問題及獲得更自然的連接曲線。另外,在修補程序上我們分成兩個階段,各使用不同的修補方法:在邊緣的修補上是使用以一個像素為單位的修補方法,而剩餘區域的修補則是使用以一個區塊為單位的修補方法,其目的是為了保持材質的特性。
Image inpainting is a technique for restoring damaged old photographs and removing undesired objects from an image. The basic idea behind the technique is to automatially fill in lost or broken parts of image by using the information from the surrounding area. The challenge of the current inpainting algorithms is to restore both of texture and structure characteristics information for large and thick damaged regions. In this study, a new image inpainting method based on Bezier curves is presented, which combines exemplar-based inpainting technique and edge-based image restoration algorithm. The proposed method use Bezier curves to reconstruct the skeleton image structure in the missing areas, it solves the limitation of edge-based restoration approach - it approximates the incoming edges with lines and circle arcs only. Besides, the inpainting process is divided into two phases, the pixel-based interpolation method is first utilized to restore the structure of the image, the patch-based method is then adopted to fill the holes for preserving the texture information. Experimental results on artificial and real images demonstrate the effectiveness and robustness of the proposed method.
誌謝...................................................i
中文摘要..............................................ii
英文摘要.............................................iii
目錄..................................................iv
圖目錄.................................................v
第一章 諸論............................................1
1.1 研究動機與背景.....................................1
1.2 論文架構...........................................4
第二章 文獻回顧........................................5
2.1 基於偏微分方程的影像修補演算法.....................5
2.2 材質合成...........................................8
2.3 結合偏微分方程和材質合成之優點的影像修補演算法....11
2.4 基於範例影像修補演算法............................16
2.5 基於邊緣影像修補演算法............................18
2.6 有效的迭代臨界值分割法............................26
第三章 所提的基於貝茲曲線影像修補演算法...............29
3.1 邊緣偵測及邊緣特徵的取得..........................30
3.2 影像骨架結構的重建................................32
3.3 以像素為單位的邊緣修補............................33
3.4 基於範例的修補....................................37
第四章 實驗結果與討論.................................39
4.1 人造影像的實驗結果................................39
4.2 真實影像的實驗結果................................45
第五章 結論與未來工作.................................53
參考文獻..............................................54
[1]M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, “Image inpainting”, Proc. of the ACM SIGGRAPH 2000, New Orleans, pp.417-424, July 2000.

[2]T. F. Chan and J. Shen, “Mathematical Models for Local Deterministic Inpaintings”, Technical Report CAM 00-11, Image Processing Research Group, UCLA, Mar 2000.

[3]T. F. Chan and J. Shen, “Non-Texture Inpainting by Curvature Driven Diffusions (CDD)”, J. Vis. Comm. Image Rep., pp.436-449, 2001.

[4]M. M. Oliveira, B. Bowen, R. McKenna, and Y. S. Chang, “Fast Digital Image Inpainting,” in Processing of the International Conference on Visualization, Imaging and Image Processing(VIIP2001),pp.261-266, 2001.

[5]A. Telea, “An Image Inpainting Technique Based on the Fast Marching Method”, Journal of Graphics Tools, 9 (1), pp.25-36, 2004.

[6]D. J. Heeger and J. R. Bergen, “Pyramid-based texture analysis/synthesis,” in Proc. ACM Conf. Computer Graphics, Los Angeles, CA, vol. 29, pp. 229–233,1995.

[7]J. Portilla and E. P. Simoncelli, “A parametric texture model based on joint statistics of complex wavelet coefficients,” International Journal of Computer Vision, vol. 40, no. 1, pp. 49–71, Oct. 2000.

[8]A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf. Computer Vision, Kerkyra, Greece, pp. 1033–1038, Sep.1999.

[9]L. Y. Wei and M. Levoy, “Fast texture synthesis using tree-structured vector quantization,” in Proc. ACM Conf. Computer Graphics, pp. 479–488, Jul. 2000.

[10]M. Ashikhmin, “Synthesizing natural textures,” in Proc. ACM Symp. Interactive 3D Graphics, pp. 217–226, Mar. 2001.

[11]S. C. Pei, Y. C. Zeng, and C. H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and Lacuna texture synthesis,” IEEE Trans. Image Process., vol. 13, no. 3, pp. 416–429, Mar. 2004.

[12]A. Efros and W. T. Freeman, “Image quilting for texture synthesis and transfer,” in Proc. ACM Conf. Computer Graphics, pp. 341–346, Aug. 2001.

[13]L. Liang, C. Liu, Y. Q. Xu, B. Guo, and H. Y. Shum, “Real-time texture synthesis by patch-based sampling,” ACM Trans. Graph., vol. 20, pp. 127–150, July 2001.

[14]V. Kwatra, A. Schodl, I. Essa, G. Turk, and A. Bobick, “Graphcut textures: Image and video synthesis using graph cuts,” in Proc. SIGGRAPH, pp. 277–286, 2003.

[15]Q. Wu and Y. Yu, “Feature matching and deformation for texture synthesis,” ACM Trans. Graph., vol. 23, no. 3, pp. 364–367, Aug 2004.

[16]M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, “Simultaneous structure and texture image inpainting”, IEEE Trans. Image Processing, vol. 12, no. 8, Aug. 2003.

[17]J. Sun, L. Yuan, J. Jia, and H.-Y. Shum, “Image Completion with Structure Propagation,” ACM Trans. on Graphics, pp.861-868, July 2005.

[18]Antonio Criminisi, Patrick Perez, and Kentaro Toyama, “Region filling and object removal by exemplar-based image inpainting”, IEEE transactions on image processing, vol. 13, no. 9, Sept. 2004

[19]A.Rares, M.J.T.Reinders, and J.Biemond, “Edge-based image restoration”, IEEE Trans. Image Processing, 14(10):1454-1468, 2005.

[20]I. Drori, D. Cohen-Or, and H. Yeshurun, “Fragment based image completion”, in ACM Trans. Graphics (SIGGRAPH), vol. 22, San Diego, CA, pp. 303–312, 2003.

[21]J. Jia and C.-K. Tang, “Image repairing: Robust image synthesis by adaptive nd tensor voting”, in Proc. Conf. Computer Vision and Pattern Recognition, Madison, June 2003.

[22]H. Yamauchi, J. Haber, and H. P. Seidel, “Image Restoration using Multiresolution Texture Synthesis and Image Inpainting”, Proc. Computer Graphics International (CGI), pp. 120-125, July 2003.

[23]H. Grossauer, “A Combined PDE and Texture Synthesis Approach to Inpainting”, European Conference on Computer Vision, LNCS 3022, pp. 214-224, 2004.

[24]BianRu Li, Yue Qi, and XuKun Shen , ”An image inpainting method”, Computer Aided Design and Computer Graphics, 2005.

[25]Nikos Komodakis and Georgios Tziritas, ”Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning,” IEEE transactions on image processing, vol. 16, no. 11, November 2007.

[26]L. Dong, G.Yu, P. Ogunbona, W. Li, ”An efficient iterative algorithm for image thresholding,” Pattern Recognition Letters, vol. 29, pp.1311-1316, 2008.
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