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CLC number: TP391

On-line Access: 2020-10-14

Received: 2019-12-24

Revision Accepted: 2020-03-14

Crosschecked: 2020-08-28

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Citations:  Bibtex RefMan EndNote GB/T7714


Li Xu


Guo Huang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.10 P.1485-1493


An improved method for image denoising based on fractional-order integration

Author(s):  Li Xu, Guo Huang, Qing-li Chen, Hong-yin Qin, Tao Men, Yi-fei Pu

Affiliation(s):  College of Electronics and Materials Engineering, Leshan Normal University, Leshan 614000, China; more

Corresponding email(s):   huangguoxuli@163.com

Key Words:  Fractional-order integral, Cauchy integral, Image denoising, Fractional gradient, Texture protection

Li Xu, Guo Huang, Qing-li Chen, Hong-yin Qin, Tao Men, Yi-fei Pu. An improved method for image denoising based on fractional-order integration[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1485-1493.

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publisher="Zhejiang University Press & Springer",

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%T An improved method for image denoising based on fractional-order integration
%A Li Xu
%A Guo Huang
%A Qing-li Chen
%A Hong-yin Qin
%A Tao Men
%A Yi-fei Pu
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900727

T1 - An improved method for image denoising based on fractional-order integration
A1 - Li Xu
A1 - Guo Huang
A1 - Qing-li Chen
A1 - Hong-yin Qin
A1 - Tao Men
A1 - Yi-fei Pu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 10
SP - 1485
EP - 1493
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900727

Given that the existing image denoising methods damage the texture details of an image, a new method based on fractional integration is proposed. First, the fractional-order integral formula is deduced by generalizing the cauchy integral, and then the approximate value of the fractional-order integral operator is estimated by a numerical method. Finally, a fractional-order integral mask operator of any order is constructed in eight pixel directions of the image. Simulation results show that the proposed image denoising method can protect the edge texture information of the image while removing the noise. Moreover, this method can obtain higher image feature values and better image vision after denoising than the existing denoising methods, because a texture protection mechanism is adopted during the iterative processing.





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