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二阶总广义变分图像修复模型及其算法 被引量：13

Second order total generalized variational inpainting model and its algorithm
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摘要 为更好地修复图像,提出了一种新的图像修复模型.通过分析新模型的性质,给出了一种有效的原始对偶修复算法.实验结果表明,相比于总变分图像修复模型,新模型在修复结果上有更高的峰值信噪比和更好的视觉效果。 In order to restore the damaged image better, this paper proposes a new second total generalized variation based image inpainting model. By analysing the properties of new model, an efficient primal-dual algorithm is introduced. Experimental results show that the new model is better than the total variation model in terms of both peak signal to noise ratio（PSNR） and visual effect.
作者 许建楼 冯象初 郝岩
机构地区 西安电子科技大学理学院 河南科技大学数学与统计学院
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2012年第5期18-23,共6页 Journal of Xidian University
基金 国家自然科学基金资助项目(60872138 61105011 11101292 61271294)
关键词 图像修复 总广义变分 总变分 原始对偶算法 image inpainting total generalized variation total variation primal-dual algorithm
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献16

  • 1Bertalmio M, Sapiro G, Caselles V, et al. Image Inpainting[C]//Proc of the ACM SIGGRAPH. New Orleans: ACM Press, 2000: 417-424.
  • 2Criminisi A, P6rez P, Toyama K. Region Filling and Object Removal by Exemplar-based Image Inpainting[J]. IEEE Trans on Image Processing, 2004, 13(9): 1200-1212.
  • 3Wu Jiying, Ruan Qiuqi. An Gaoyun. Exemplar-based Image Completion Model Employing PDE Corrections [J]. Informatica, 2010, 21(2): 259-276.
  • 4Xu Zongben, Sun Jian. Image Inpainting by Patch Propagation Using Patch Sparsity [J]. IEEE Trans on Image Processing, 2010, 19(5): 1153-1165.
  • 5Masnou S. Disocclusion: a Variational Approach Using Level Lines[J]. IEEE Trans on Image Processing, 2002, 11: 68- 76.
  • 6Levin A, Zomet A, Weiss Y. Learning How to Inpait from Global Image Statistics[C]//IEEE International Conference on Computer Vision. France: IEEE, 2003: 305-312.
  • 7郝岩,冯象初,许建楼.一种非局部扩散的图像修复模型[J].西安电子科技大学学报,2010,37(5):825-828. 被引量：5
  • 8张伟斌,冯象初,王卫卫.带Besov忠诚项的图像去噪变分模型[J].西安电子科技大学学报,2011,38(3):155-158. 被引量：2
  • 9Rudin L, Osher S, Fatemi E. Nonlinear Total Veriation Based Noise Removal Algorithms [J]. Physica D, 1992, 60: 259-268.
  • 10Chan T, Shen J. Mathematical Models of Local Non-texture Inpaintings[J]. SIAM Journal on Applied Math, 2002, 62 (3) : 1019-1043.

二级参考文献24

  • 1Bertalmio M,Sapiro G,Ballester C,et al.Image Inpainting[C] //Proceedings SIGGRAPH,2000,Computer Graphics Proceedings,Annual Conference Series.Reading:Addison-Wesley,2000:417-424.
  • 2Rudin L,Osher S,Fatemi E.Nonlinear Total Veriation Based Noise Removal Algorithms[J].Physica D,1992(60):259-268.
  • 3Chan T F,Shen J.Mathematical Models of Local Non-texture Inpaintings[J].SIAM J Appl Math,2002,62(3):1019-1043.
  • 4Chan T F,Shen J.Variational Image Inpainting[J].Communications on Pure and Applied Mathematics,2005(58):579-619.
  • 5Chan T F,Shen J.Non-texture Inpainting by Curvature Driven Diffusiona (CDD)[J].J Visual Comm Image Reg,2001,12(4):436-449.
  • 6Gilboa G,Osher S.Nonlocal Operators with Applications to Image Processing[DB/OL].[2009-08-07].ftp://ftp.math.ucla.edu/pub/camreport/cam07-23.pdf.
  • 7Lou Y,Zhang X,Osher S,et al.Image Recovery Via Nonlocal Operators[DB/OL].[2009-10-12].ftp://ftp.math.ucla.edu/pub/camreport/cam08-35.pdf.
  • 8Li Li,Han Yu.Nonlocal Curvature-driven Diffusion Model for Image Inpainting[C] //The Fifth International Conference on Information Assurance and Security:Vol 2.Xi'an:IEEE Computer Society,2009:513-516.
  • 9Huang Yumei,Ng M K,Wen Youwei.A New Total Variation Method for Multiplicative Noise Removal[J].SIAM J Image Sciences,2009,2(1):20-40.
  • 10Durand S,Fadili J,Nikolova M.Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients[J].J Math Imaging Vis,2010(36):201-226.

共引文献5

同被引文献114

  • 1王枚,潘国华,王国宏,尤晶晶.运动和散焦模糊图像的复原方法及其应用研究[J].激光与红外,2007,37(10):1120-1122. 被引量：6
  • 2Bertalmio M, Sapiro G, Caselles V, et al.. Image inpainting [C]. Proceedings of the ACM SIGGRAPH, New Orleans, ACM Press, 2000: 417-424.
  • 3Chan T and Shen J. Non-texture inpainting by curvature driven diffusion (CDD)[J]. Journal of Visual Communication and Image Representation, 2001, 12(4): 436-449.
  • 4Chan T and Shen J. Mathematical models of local non- texture inpaintings[J]. SIAM Journal on Applied Mathematics, 2002, 62(3): 1019-1043.
  • 5Xu Zong-ben and Sun Jian. Image inpainting by patch propagation using patch sparsity[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1153-1165.
  • 6Chan T, Shen J, and Zhou H. Total variation wavelet inpainting[J]. Journal of Mathematical Imaging and Vision, 2006, 25(1): 107-125.
  • 7Chan R, Wen Y, and Yip A. A fast optimization transfer algorithm for image inpainting in wavelet domains[J]. [EEE Transactions on Image Processing,2009, 18(7): 1467-1476.
  • 8Wen Y, Chan R, and Yip A. A primal-dual method for total variation based wavelet domain inpainting[J]. IEEE Transactions on Image Processing, 2012, 21(1): 106-114.
  • 9Zhang Xiao-qun and Chan T. Wavelet inpainting by nonlocal total variation[J]. Inverse Problems and Imaging, 2010, 4(1): 1-20.
  • 10Candues E J, Wakin M B, and Boyd S P. Enhancing sparsity by reweighted LL minimization[J], Journal of Fourier Analysis and Applications, 2008, 14(5 6): 877-905.

引证文献13

二级引证文献57

西安电子科技大学学报
2012年 第5期

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