A NOVEL NOISE REMOVAL METHOD BASED ON FRACTIONAL ANISOTROPIC DIFFUSION AND SUBPIXEL APPROACH

Yingtao Zhang (), H. D. Cheng (), Yangquan Chen () and Jianhua Huang ()
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Yingtao Zhang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
H. D. Cheng: Department of Computer Science, Utah State University, Logan, UT 84322, U.S.A
Yangquan Chen: Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84322, U.S.A
Jianhua Huang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

New Mathematics and Natural Computation (NMNC), 2011, vol. 07, issue 01, 173-185

Abstract: Partial differential equations (PDE) have been successfully and widely applied to image processing and computer vision. Anisotropic diffusion is an approach to remove noise based on nonlinear PDE. Many anisotropic methods have been studied; however, they suffer two major drawbacks: blurring and staircasing effects degrading the performance of noise removal filters. To overcome such problems, in this paper, a novel and efficient method for image denoising based on fractional-order anisotropic diffusion and subpixel approach is proposed. Numerical computation is implemented by using the subpixel fractional partial difference (SFPD) approach to increase the flexibility and accuracy. The experimental results demonstrate that the proposed approach can achieve higher signal-to-noise ratio (SNR) and its performance is much better than that of the existing filters.

Keywords: Anisotropic diffusion (AD); subpixel; fractional-order PDE; noise removal; signal-to-noise ratio (SNR); subpixel fractional partial difference (SFPD) (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S1793005711001871

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