期刊文献+

Variational Models for Color Image Inpainting and Their Split Bregman Algorithms

Variational Models for Color Image Inpainting and Their Split Bregman Algorithms
下载PDF
导出
摘要 Image inpainting is an important part of image science,but in the past,researches were focused on gray value image inpainting.In this paper,we investigate the inpainting effects of some variational models of color image diffusion.Five variational models for color image inpainting are proposed and their Split Bregman algorithms are designed.Their regularizers are LTV(Layered Total Variation) regularizer,CTV(Color Total Variation) regularizer,MTV(Multichannel Total Variation) regularizer,PA(Polyakov Action) regularizer and RPA(Reduced Polyakov Action) regularizer respectively.In order to compare their performances,we use the same data term...Some numerical experiments show the differences of the above mentioned models for color image inpainting. Image inpainting is an important part of image science, but in the past, researches were focused on gray value image inpainting. In this paper, we investigate the inpainting effects of some variational models of color image diffusion. Five variational models for color image inpainting are proposed and their Split Bregman algorithms are designed. Their regularizers are LTV (Layered Total Variation) regularizer, CTV(Color Total Variation) regularizer, MTV(Multichannel Total Variation) regularizer, PA (Polyakov Action) regularizer and RPA (Reduced Polyakov Action) regularizer respectively. In order to compare their performances, we use the same data term... Some numerical experiments show the differences of the above mentioned models for color image inpainting.
出处 《科技信息》 2011年第6期153-156,共4页 Science & Technology Information
关键词 图像修复 色彩 图像处理 分层全变差 Color image lnpainting Split Bregman algorithm
  • 相关文献

参考文献23

  • 1M. Bertalmio, G., Sapiro, L.-T. Cheng, S. Osher, Image Iripainting, In ACM SIGGRAPH, pages 417 - 424, 2000.
  • 2L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithm. Physica D, 60:(1-4), 259-268,1992.
  • 3T.Chan F, J.Shen Mathematical models for local nontexture inpaintings[J]. SIAM J. Appl. Math. 62 (3): 1019 - 1043, 2001.
  • 4T. Chan,J. Sheng. Non-texture inpainting by curvature driven diffusions (CDD)[J ] Journal of Visual Communication and Image 1Lepre. sentation, 12(4) : 436449,2001.
  • 5G. Sapiro, D. L. Ringach, Anisotropic Diffusion of Multi-valued Images with Applications to Color Filtering, IEEE Transactions on Image Processing, 5(11): 1582-1586,1996.
  • 6P. Blomgren and T. F. Chan. Color TV: Total variation methods for restoration of vector-valued images. IEEE Trans. Image Processing, 7: 304 - 309, 1998.
  • 7J. F. Aujol and S. H. Kang, Color Image Decomposition and Restoratign, Journal of Visual Communication and Image Representation,17(4): 916-928, 2006.
  • 8J. Yang, w. Yin, Y. Zhang and Y.Wang, A fast algorithm for edge-preserving variational multichannet image restoration. SIAM Journal on Imaging Sciences, 2(2):569-592, 2009.
  • 9L.Rudin , S. Osher, Total variation based image restoration with free local constlzaints [ A] In : Proceedings Of IEEE International Conference on Image Processing, Austin, 1:31-35, 1994.
  • 10Zhang Quanling, Wang Maoxiang, Wu Lenanl A new algorithmin image restoration [J ] Electronic Engineer , 25 (11) :12-14 (in Chi- nese) , 1999.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部