Advertisement

A Study of Images Denoising Based on Two Improved Fractional Integral Marks

  • Changxiong Zhou
  • Tingqin Yan
  • Wenlin Tao
  • Shufen Lui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

In this paper, applying fractional calculus Grümwald-Letnikov definition, a novel image denoising approach based on two improved fractional integral masks is proposed, Two structures of 3×3 fractional integral masks which center is the processed pixel are constructed and discussed. The denoising performance of the proposed fractional integral marks (FIM1 and FIM2) is measured using experiments according to subjective and objective standards of visual perception and SNR values. The simulation results show that SNR of FIM1 and FIM2 is prior to the mean filter and the method in Ref. [8].

Keywords

image denoising fractional integral Grümwald-Letnikov definition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Special issue: Fractional Signal Processing and Applications. Signal Processing 83(11), 2285–2286 (2003)Google Scholar
  2. 2.
    Tatom, F.B.: The Relationship between Fractional Calculus and Fractals. Fractals 3(1), 217–229 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Pu, Y., Zhou, J., Yuan, X.: Fractional Differential Mask: a Fractional Differential-based Approach for Multi-scale Texture Enhancement. IEEE Transactions on Image Processing 19(2), 491–511 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Oldham, K.B., Spanier, J.: The Fractional Calculus. Academic Press, New York (1974)zbMATHGoogle Scholar
  5. 5.
    Mathieu, B., Melchior, P., Oustaloup, A.: Fractional Differentiation for Edge Detection. Signal Processing 83(11), 2421–2432 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Pu, Y., Wang, W.: Fractional Differential Masks of Digital Image and Their Numerical Implementation Algorithms. Acta Automatica Sinica 33(11), 1128–1135 (2007)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Pu, Y., Wang, W., Zhou, J., et al.: Fractional Differential Approach to Detecting Textural Features of Digital Image and its Fractional Differential Filter Implementation. Sci. China Ser. F, Inf. Sci. 51(9), 1319–1339 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Huang, G., Pu, Y., Chen, Q., Zhou, J.: Research on Image Denoising Based on Fractional Integral. System Engineering and Electronics 33(4), 925–932 (2011) (in Chinese) zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Changxiong Zhou
    • 1
  • Tingqin Yan
    • 1
    • 2
  • Wenlin Tao
    • 1
  • Shufen Lui
    • 1
  1. 1.Department of Electronic and Informational EngineeringSuzhou Vocational UniversitySuzhouChina
  2. 2.Suzhou Key Lab. of Digital Design & Manufacturing TechnologyChina

Personalised recommendations