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A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM

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Intelligent Computing Methodologies (ICIC 2018)

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Abstract

A novel image denoising algorithm based on non-subsampled contourlet transform (NSCT) and modified non-local mean (NLM) is proposed. First, we utilize NSCT to decompose the images to obtain the high frequency coefficients. Second, the high frequency coefficients are used for modified NLM denoising. Finally, the NLM weight values are calculated by modified bisquare function instead of Gaussian kernel function of the traditional NLM, and each noise coefficient is corrected to get the denoised image. According to results of the simulation experiment, the denoising results of the proposed algorithm obtain higher peak signal-to-noise ratio (PSNR) and better retains structural information of image in subjective vision.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (No. 61502356), by Hubei Province Natural Science Foundation of China (No. 2018CFB526).

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Authors and Affiliations

  1. Department of Information Engineering, City College of Wuhan University of Science and Technology, Wuhan, 430083, China

    Huayong Yang & Xiaoli Lin

  2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xiaoli Lin

Authors
  1. Huayong Yang
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  2. Xiaoli Lin
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Corresponding author

Correspondence to Huayong Yang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Yang, H., Lin, X. (2018). A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_71

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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