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Mathematical Problems in Engineering
Volume 2016, Article ID 5130346, 12 pages
http://dx.doi.org/10.1155/2016/5130346
Research Article

A Cartoon-Texture Decomposition Based Multiplicative Noise Removal Method

1School of Mathematics and Statistics, Xidian University, Xi’an 710026, China
2School of Mathematical Science, Henan Institute of Science and Technology, Xinxiang 453003, China

Received 23 April 2016; Revised 29 June 2016; Accepted 26 July 2016

Academic Editor: Maria L. Gandarias

Copyright © 2016 Chenping Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. Rudin, P.-L. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, pp. 103–119, Springer, New York, NY, USA, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  2. G. Aubert and J.-F. Aujol, “A variational approach to removing multiplicative noise,” SIAM Journal on Applied Mathematics, vol. 68, no. 4, pp. 925–946, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  3. J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM Journal on Imaging Sciences, vol. 1, no. 3, pp. 294–321, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. Y.-M. Huang, M. K. Ng, and Y.-W. Wen, “A new total variation method for multiplicative noise removal,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 20–40, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  5. S. Durand, J. Fadili, and M. Nikolova, “Multiplicative noise removal using L1 fidelity on frame coefficients,” Journal of Mathematical Imaging and Vision, vol. 36, no. 3, pp. 201–226, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Hao, X. Feng, and J. Xu, “Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation,” Signal Processing, vol. 92, no. 6, pp. 1536–1549, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. Y.-M. Huang, L. Moisan, M. K. Ng, and T. Zeng, “Multiplicative noise removal via a learned dictionary,” IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4534–4543, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. T. Teuber and A. Lang, “Nonlocal filters for removing multiplicative noise,” in Scale Space and Variational Methods in Computer Vision, A. M. Bruckstein, B. M. ter Haar Romeny, A. M. Bronstein, and M. M. Bronstein, Eds., vol. 6667 of Lecture Notes in Computer Science, pp. 50–61, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  9. F. Dong, H. Zhang, and D.-X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Mathematical and Computer Modelling, vol. 55, no. 3-4, pp. 939–954, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  10. X.-L. Zhao, F. Wang, and M. K. Ng, “A new convex optimization model for multiplicative noise and blur removal,” SIAM Journal on Imaging Sciences, vol. 7, no. 1, pp. 456–475, 2014. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  11. W. Yin, D. Goldfarb, and S. Osher, “The total variation regularized model for multiscale decompostion,” Multiscale Modeling and Simulation, vol. 6, pp. 190–211, 2007. View at Google Scholar
  12. A. Chambolle, “An algorithm for total variation minimization and applications,” Journal of Mathematical Imaging and Vision, vol. 20, no. 1-2, pp. 89–97, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus