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 - <p style="text-align: justify;">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.</p>