Volume 11, Issue 4
An Efficient Feature-Preserving Image Denoising Algorithm Based on a Spatial-Fractional Anisotropic Diffusion Equation

Maoyuan Xu & Xiaoping Xie

East Asian J. Appl. Math., 11 (2021), pp. 788-807.

Published online: 2021-08

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  • Abstract

An efficient feature-preserving fractional image denoising algorithm based on a nonlinear spatial-fractional anisotropic diffusion equation is proposed. Two-sided Grünwald-Letnikov fractional derivatives used in the PDE model are suitable to depict the local self-similarity of images. The short memory principle is employed to simplify the approximation scheme. Experimental results show that the method has an extremely high structural retention property and keeps a remarkable balance between noise removal and feature preserving.

  • Keywords

Image denoising, feature preserving, spatial-fractional diffusion equation, two-sided derivative, Grünwald-Letnikov derivative

  • AMS Subject Headings

65M10, 78A48

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{EAJAM-11-788, author = {Maoyuan and Xu and and 17768 and and Maoyuan Xu and Xiaoping and Xie and and 17769 and and Xiaoping Xie}, title = {An Efficient Feature-Preserving Image Denoising Algorithm Based on a Spatial-Fractional Anisotropic Diffusion Equation}, journal = {East Asian Journal on Applied Mathematics}, year = {2021}, volume = {11}, number = {4}, pages = {788--807}, abstract = {

An efficient feature-preserving fractional image denoising algorithm based on a nonlinear spatial-fractional anisotropic diffusion equation is proposed. Two-sided Grünwald-Letnikov fractional derivatives used in the PDE model are suitable to depict the local self-similarity of images. The short memory principle is employed to simplify the approximation scheme. Experimental results show that the method has an extremely high structural retention property and keeps a remarkable balance between noise removal and feature preserving.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.081220.270421}, url = {http://global-sci.org/intro/article_detail/eajam/19372.html} }
TY - JOUR T1 - An Efficient Feature-Preserving Image Denoising Algorithm Based on a Spatial-Fractional Anisotropic Diffusion Equation AU - Xu , Maoyuan AU - Xie , Xiaoping JO - East Asian Journal on Applied Mathematics VL - 4 SP - 788 EP - 807 PY - 2021 DA - 2021/08 SN - 11 DO - http://doi.org/10.4208/eajam.081220.270421 UR - https://global-sci.org/intro/article_detail/eajam/19372.html KW - Image denoising, feature preserving, spatial-fractional diffusion equation, two-sided derivative, Grünwald-Letnikov derivative AB -

An efficient feature-preserving fractional image denoising algorithm based on a nonlinear spatial-fractional anisotropic diffusion equation is proposed. Two-sided Grünwald-Letnikov fractional derivatives used in the PDE model are suitable to depict the local self-similarity of images. The short memory principle is employed to simplify the approximation scheme. Experimental results show that the method has an extremely high structural retention property and keeps a remarkable balance between noise removal and feature preserving.

Maoyuan Xu & Xiaoping Xie. (2021). An Efficient Feature-Preserving Image Denoising Algorithm Based on a Spatial-Fractional Anisotropic Diffusion Equation. East Asian Journal on Applied Mathematics. 11 (4). 788-807. doi:10.4208/eajam.081220.270421
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