Volume 13 Number 3 (Mar. 2018)
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JSW 2018 Vol.13(3): 155-167 ISSN: 1796-217X
doi: 10.17706/jsw.13.3.155-167

Iterative Fractional Integral Denoising Based on Detection of Gaussian Noise

Yuanxiang Jiang1, Rui Yuan1*,Yuqiu Sun1, Jinwen Tian2
1School of Information and Mathematics, Yangtze University, Jingzhou, Hubei, China.
2School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.


Abstract—In an image with noise, any operation of denoising for a non-noise pixel will change original information. Recent studies show that the denoising algorithms based on noise achieve impressive performance. Meanwhile, because of the characteristics of fractional calculus, the edge information will be retained, and the smooth texture is enhanced while noise is removed. In this paper, an iterative fractional integral denoising algorithm based on noise is proposed. To begin with, we introduce and analyze the noise detection algorithm based on fractional differential gradient and fractional integral denoising from the theoretical point of view. In particular, logical product is made through image of fractional differential gradient to obtain noise position image, thus achieving noise detection. Next, fractional integral denoising algorithms based on tradition and noises are finished. Then, iterative algorithm is used to do multiple searches of noise and integral denoising. In addition, several traditional denoising algorithms and denoising based on noise points are compared to confirm the practicability and feasibility of noise detection algorithm as well as the effectiveness of denoising algorithms based on noise. Finally, different denoising methods are compared to show the characteristic of iterative fractional integral denoising based on noise. By comparing the image visualization and evaluation parameters after processing, it is shown from the experiment results that the method proposed in this paper has good effect of denoising in both subjective and objective aspects.

Index Terms—denoisingalgorithm, differential gradient,noise detection, fractional integral

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Cite: Yuanxiang Jiang, Rui Yuan,Yuqiu Sun, Jinwen Tian, "Iterative Fractional Integral Denoising Based on Detection of Gaussian Noise," Journal of Software vol. 13, no. 3, pp. 155-167, 2018.

General Information

ISSN: 1796-217X
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jsw@iap.org
  • Sep 21, 2018 News!

    Papers published in JSW Vol. 13, No. 1- Vol. 13 No. 8 have been indexed by DBLP.    [Click]

  • Aug 24, 2018 News!

    Vol.12, No.8- Vol.13, No.5 has been indexed by EI (Inspec).   [Click]

  • Aug 01, 2018 News!

    [CFP] 2018 the annual meeting of JSW Editorial Board, ICSTE 2018, will be held in Kuala Lumpur, Malaysia, October 27-29, 2018.   [Click]

  • Sep 29, 2018 News!

    The papers published in Vol.13, No. 9 have all received dois from Crossref. 

  • Sep 21, 2018 News!

    Vol 13, No. 9 has been published with online version 4 original aritcles from 3 countries are published in this issue.     [Click]