KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

  • Received : 2016.05.17
  • Accepted : 2016.12.04
  • Published : 2017.01.31
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Abstract

Recently, the method based on fractional order partial differential equation has been used in image processing. Usually, the optional order of fractional differentiation is determined by a lot of experiments. In this paper, a denoising model is proposed based on adaptive fractional order anisotropic diffusion. In the proposed model, the complexity of the local image texture is reflected by the local variance, and the order of the fractional differentiation is determined adaptively. In the process of the adaptive fractional order model, the discrete Fourier transform is applied to compute the fractional order difference as well as the dynamic evolution process. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the proposed image denoising algorithm is better than that of other some algorithms. The proposed algorithm not only can keep the detailed image information and edge information, but also obtain a good visual effect.

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References

  1. P Perona, J Malik, "Scale-space and edge detecting using anisotropic diffusion," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. 7, pp. 629-639, July, 1990. https://doi.org/10.1109/34.56205
  2. L Rudin, S Osher, E Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D-Nonlinear Phenom, vol. 60, no. 1-4, pp. 259-268, 1992. https://doi.org/10.1016/0167-2789(92)90242-F
  3. T Chan, S Esedoglu, Park F, Recent developments in total variation image restoration, 1nd Edition, New York, 2005.
  4. Giovanni Motta, "The iDUDE framework for grayscale image denoising," IEEE Transactions on Image Processing, vol. 20, no. 1, pp. 1-21, September, 2011. https://doi.org/10.1109/TIP.2010.2053939
  5. Gong Yuanhao, Sbalzarini Ivo F., "Local weighted Gaussian curvature for image processing," in Proc. of 20th IEEE International Conference on Image Processing, pp. 534-538, September 15-18, 2013.
  6. Wang Jiefei, Chen Yupeng, Li Tao, Lu Jian,and Shen Lixin, "A residual-based kernel regression method for image denoising," Mathematical Problems in Engineering, pp. 1-13, March, 2016.
  7. Zhang Wenxue, Cao Yongzhen, Zhang Rongxin, Li Lingling, and Wen Yunlei, "Image denoising via L0 gradient minimization with effective fidelity term," Mathematical Problems in Engineering, pp. 1-11, December, 2015.
  8. Cui Lihong, Wang Zhan, Cen Yigang, "An extension of the interscale SURE-LET approach for image denoising," International Journal of Advanced Robotic Systems, vol. 11, no. 1, pp. 257-267, January, 2014.
  9. Zhao De, He Chuanjiang, and Chen Qiang, "Anisotropic diffusion model combined with local entropy," Pattern Recognition and Artificial Intelligence, vol. 25, no. 4, pp. 642-647, April, 2012.
  10. Sajjad Mazhar, Ahn Chang-Won, Jung Jin-Woo, "Iris image enhancement for the recognition of non-ideal iris images," KSII Transactions on Internet and Information Systems, vol. 10, no. 4, pp. 1904-1926, April, 2016. https://doi.org/10.3837/tiis.2016.04.025
  11. Yu Hancheng, Li Aiting, "Real-time non-local means image denoising algorithm based on local binary descriptor," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 825-836, February, 2016. https://doi.org/10.3837/tiis.2016.02.021
  12. Zhou Yan, Li Qingwu, Huo Guanying, "Human visual system based automatic underwater image enhancement in NSCT domain," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 837-856, February, 2016. https://doi.org/10.3837/tiis.2016.02.022
  13. G Ghimpeţeanu, T Batard, M Bertalmio, "A decomposition framework for image denoising algorithms," Image Processing IEEE Transactions on, vol. 25, no. 1, pp. 388-399, January, 2016. https://doi.org/10.1109/TIP.2015.2498413
  14. VBS Prasath, R Delhibabu, "Image restoration with fuzzy coefficient driven anisotropic diffusion," in Proc. of 5th International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 145-155, December 18-20, 2015.
  15. Wang Liping, Zhou Shangbo, and Karim Awudu, "Super-resolution image reconstruction method using homotopy regularization," Multimed Tools Applications, pp. 1-24, September, 2015.
  16. Bai Jian , Feng Xiangchu, "Fractional-order anisotropic diffusion for image denoising," IEEE Transactions on Image Processing, vol. 16, no. 10, pp. 2492-2502, October, 2007. https://doi.org/10.1109/TIP.2007.904971
  17. Che Jin, Guan Qian, and Wang Xiyuan, "Image denoising based on adaptive fractional partial differential equations," in Proc. of 6th International Congress on Image and Signal Processing, pp. 288-292, December 16-18, 2013.
  18. Li Bo, Xie Wei, "Adaptive fractional differential approach and its application to medical image enhancement," Computers and Electrical Engineering, vol. 45, pp. 324-335, July, 2015. https://doi.org/10.1016/j.compeleceng.2015.02.013
  19. Pu Yifei, Slarry Patrick, and Zhou Jiliu, "Fractional partial differential equation denoising models for texture image," Science China Information Sciences, vol. 57, no. 7, pp. 1-19, May, 2014.
  20. Yin Xuehui, Zhou Shangbo, "Image structure-preserving denoising based on difference curvature driven fractional nonlinear diffusion," Mathematical Problems in Engineering, pp. 1-16, April, 2015.
  21. Zachevsky, Ido, Zeevi, Yehoshua Y, "Statistics of natural stochastic textures and their application in image denoising," IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 2130-2145, May, 2016. https://doi.org/10.1109/TIP.2016.2539689
  22. Chen Yiming, Wei Yanqiao, Liu Dayan, "Variable-order fractional numerical differentiation for noisy signals by wavelet denoising," Journal of computational physics, vol. 311, pp. 338-347, April, 2016. https://doi.org/10.1016/j.jcp.2016.02.013
  23. KB Oldham, J Spanier, "The fractional calculus," Mathematical Gazette, vol. 56, no. 247, pp. 396-400, January, 1974.
  24. ER Love, "Fractional Derivatives of Imaginary Order," Journal of the London Mathematical Society, vol. s2-3, no. 2, pp. 241-259, February, 1971. https://doi.org/10.1112/jlms/s2-3.2.241
  25. Wang Chengliang, Lan Libin, and Zhou Shangbo, "Adaptive fractional differential and its application to image texture enhancement," Journal of Chongqing University, vol. 34, no. 2, pp. 32-37, February, 2011.
  26. Huang Guo, Chen Qingli, and Xu Li, "Realization of adaptive image enhancement with variable fractional order differential," Journal of Shenyang University of Technology, vol. 34, no. 4, pp. 446-454, April, 2012.
  27. Pu Yifei, Research on application of fractional calculus to latest signal analysis and processing, Sichuan University, 2006.

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