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Application in Image Denoising Using Fractional Total Variation Theory

  • Guo Huang
  • Qing-li Chen
  • Tao Men
  • Xiu-Qiong Zhang
  • Hong-Ying Qin
  • Li Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 506)

Abstract

Aiming at the existing problems that the image denoising algorithm based on integer-order partial differential equation could lost part of edge and texture information. This image denoising algorithm based on fractional variational theory was proposed by the theory of fractional calculus and partial differential equation. The denoising model proposed in this paper introduces and implements the numeric computation of the fractional variation partial differential equations by constructing the fractional differential mask operators along eight directions of image. The simulation data prove that the image denoising algorithm based on fractional variation theory compared with the traditional image denoising algorithm could better retain the edge and texture detail information, obtain visual effect, and properly improve the signal-to-noise ratio.

Keywords

Fractional total variation Fractional gradient amplitude Fractional partial differential equations Fractional derivative mask operator Image denoising 

Notes

Acknowledgment

This work is supported by Sichuan province science and technology department application foundation project (2016JY0238) and Scientific Research Fund of Sichuan Province Education Department(18ZA0363,18ZB0374, 18ZB0376) and Key Research Base for Social Science in Sichuan Province-Sichuan Tourism Development Research Center Project (LYC15-09).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Guo Huang
    • 1
  • Qing-li Chen
    • 1
  • Tao Men
    • 1
  • Xiu-Qiong Zhang
    • 1
  • Hong-Ying Qin
    • 1
  • Li Xu
    • 2
  1. 1.School of Computer ScienceLeshan Normal UniversityLeshanChina
  2. 2.School of Physics and ElectronicsLeshan Normal UniversityLeshanChina

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