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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 981932, 8 pages
http://dx.doi.org/10.1155/2014/981932
Research Article

Image Enhancement under Data-Dependent Multiplicative Gamma Noise

1Department of Mathematical and Computational Sciences, National Institute of Technology, Karnataka 575025, India
2Department of Electronics and Communications Engineering, National Institute of Technology, Karnataka 575025, India

Received 13 February 2014; Accepted 19 May 2014; Published 1 June 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Jidesh Pacheeripadikkal and Bini Anattu. 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. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Kim, V. R. Calvert, and S. Kim, “Preservation of fine structures in pde-based image denoising,” Advances in Numerical Analysis, vol. 2012, Article ID 750146, 19 pages, 2012. View at Publisher · View at Google Scholar
  3. X. Li, L. Shao, R. Yan, and Y. Liu, “From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms,” IEEE Transactions on Cybernetics, 2013.
  4. D. Bertaccini, R. H. Chan, S. Morigi, and F. Sgallari, “An adaptive norm algorithm for image restoration,” Lecture Notes in Computer Science, vol. 6667, pp. 194–205, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. R. H. Chan, A. Lanza, S. Morigi, and F. Sgallari, “An adaptive strategy for the restoration of textured images using fractional order regularization,” Numerical Mathematics, vol. 6, no. 1, pp. 276–296, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1–4, pp. 259–268, 1992. View at Scopus
  7. A. Marquina and S. Osher, “Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal,” SIAM Journal on Scientific Computing, vol. 22, no. 2, pp. 387–405, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Aubertt 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 Scopus
  9. S. Setzer, G. Steidl, and T. Teuber, “Deblurring Poissonian images by split Bregman techniques,” Journal of Visual Communication and Image Representation, vol. 21, no. 3, pp. 193–199, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. P. L. Lions, L. I. Rudin, and S. Osher, “Multiplicative denoising and deblurring: theory andalgorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, pp. 103–120, Springer, 2003.
  11. L. Xiao, L.-L. Huang, and Z.-H. Wei, “Multiplicative noise removal via a novel variational model,” Eurasip Journal on Image and Video Processing, vol. 2010, Article ID 250768, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. M. K. Ng, M. Huang, and Y. Y. W. Wen, “A new total variation method for multiplicative noise removal,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 22–40, 2009.
  13. P. Jidesh and A. A. Bini, “A complex diffusion driven approach for removing data-dependent multiplicative noise,” in Proceedings of the Pattern Recognition and Machine Intelligence, vol. 8251 of Lecture Notes in Computer Science, pp. 284–289, 2013.
  14. P. Jidesh, “A convex regularization model for image restoration,” Computers and Electrical Engineering, 2014. View at Publisher · View at Google Scholar
  15. S. Osher and L. I. Rudin, “Feature-oriented image enhancement using shock filters,” SIAM Journal on Numerical Analysis, vol. 27, no. 4, pp. 919–940, 1990. View at Scopus
  16. L. Alvarez and L. Mazorra, “Signal and image restoration using shock filters and anisotropic diffusion,” SIAM Journal on Numerical Analysis, vol. 31, no. 2, pp. 590–605, 1994. View at Scopus
  17. G. Gilboa, N. A. Sochen, and Y. Y. Zeevi, “Regularized shock filters and complex diffusion,” in Proceedings of the Computer Vision (ECCV '02), pp. 399–413, 2002.
  18. P. Jidesh and S. George, “Curvature driven diffusion coupled with shock for image enhancement/reconstruction,” International Journal of Signal and Imaging Systems Engineering, vol. 4, no. 4, pp. 238–247, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Jidesh and S. George, “Shock coupled fourth-order diffusion for image enhancement,” Computers and Electrical Engineering, vol. 38, no. 5, pp. 1262–1277, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Blomgren, T. F. Chan, and P. Mulet, “Extensions to total variation denoising,” in Advanced Signal Processing: Algorithms, Architectures and Implementations VII, Proceedings of the SPIE, pp. 367–375, July 1997. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,” Journal of Computational Physics, vol. 79, no. 1, pp. 12–49, 1988. View at Scopus
  22. W. K. Pratt, Digital Image Processing, Wiley, 4th edition, 2007.
  23. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus