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

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Intelligent Computing Technology (ICIC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

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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].

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References

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Author information

Authors and Affiliations

  1. Department of Electronic and Informational Engineering, Suzhou Vocational University, Suzhou, Jiangsu, 215104, China

    Changxiong Zhou, Tingqin Yan, Wenlin Tao & Shufen Lui

  2. Suzhou Key Lab. of Digital Design & Manufacturing Technology, Jiangsu, 215104, China

    Tingqin Yan

Authors
  1. Changxiong Zhou
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  2. Tingqin Yan
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  3. Wenlin Tao
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  4. Shufen Lui
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    Changjun Jiang

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona, 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhou, C., Yan, T., Tao, W., Lui, S. (2012). A Study of Images Denoising Based on Two Improved Fractional Integral Marks. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_50

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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