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Journal of Sensors
Volume 2016 (2016), Article ID 2019569, 6 pages
http://dx.doi.org/10.1155/2016/2019569
Research Article

An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization

1Information Engineering Department, Hubei University for Nationalities, Enshi 445000, China
2Digital Media College, Sichuan Normal University, Chengdu 610068, China

Received 19 March 2015; Revised 1 July 2015; Accepted 12 July 2015

Academic Editor: Marco Anisetti

Copyright © 2016 Li Guo 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. J. Weickert, “A review of nonlinear diffusion filtering,” in Scale-Space Theory in Computer Vision, vol. 1252, pp. 1–28, Springer, 1997. View at Google Scholar
  2. T. F. Chan and S. Esedoglu, “Aspects of total variation regularized L1 function approximation,” Tech. Rep. 04-07, UCLA Mathematics Department, 2004. View at Google Scholar
  3. C. Bo and Z. Li, “Symmetric four order PDE denoising algorithm,” Computer Engineering, vol. 34, no. 13, pp. 188–189, 2008. View at Google Scholar
  4. D. H. Xu and R. S. Wang, “Imaging denoising based on non-local regularization,” The Research and Application of Computer, vol. 26, no. 12, pp. 4830–4832, 2009. View at Google Scholar
  5. M. D. Gupta and S. Kumar, “Non-convex P-norm projection for robust sparsity,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 1593–1600, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. G. J. Liu and X. P. Zeng, “Map image adaptive regularization denoising,” Journal of Chongqing University, vol. 35, no. 10, pp. 63–67, 2012. View at Google Scholar
  7. X. W. Liu and L. H. Huang, “A new nonlocal total variation regularization algorithm for image denoising,” Mathematics and Computers in Simulation, vol. 97, no. 3, pp. 224–233, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. M. J. Chen, P. X. Yang, and J. Wang, “Adaptive image denoising model based regularization and TV fidelity,” Journal of Chongqing Post and Telecommunication, vol. 23, no. 5, pp. 621–625, 2011. View at Google Scholar
  9. S. Suman, “Image denoising using new adaptive based median filter,” Signal & Image Processing, vol. 5, no. 4, pp. 1–13, 2014. View at Publisher · View at Google Scholar
  10. J. Yan and W. S. Lu, “Imaging denoising by generalized total variation regularization and least squares fidelity,” Multidimensional Systems and Signal Processing, vol. 20, no. 1, pp. 89–97, 2015. View at Google Scholar
  11. A. Anilet and C. Hati, “Image denoising method using curvelet transform and wiener filter,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 1, pp. 6943–6950, 2014. View at Google Scholar
  12. Z. Liu, E. Blasch, Z. Xue, J. Zhao, R. Laganiere, and W. Wu, “Objective assessment of multiresolution fusion algorithms for context enhancement in night vision: a comparative study,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 94–109, 2011. View at Google Scholar
  13. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D, vol. 60, no. 1–4, pp. 259–268, 1992. View at Publisher · View at Google Scholar · View at Scopus