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Accurate Prior Modeling in the Locally Adaptive Window-Based Wavelet Denoising

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Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9772))

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Abstract

The locally adaptive window-based (LAW) denoising method has been extensively studied in literature for its simplicity and effectiveness. However, our statistical analysis performed on its prior estimation reveals that the prior is not estimated properly. In this paper, a novel maximum likelihood prior modeling method is proposed for better characterization of the local variance distribution. Goodness of fit results shows that our proposed prior estimation method can improve the model accuracy. A modified LAW denoising algorithm is then proposed based on the new prior. Image denoising experimental results demonstrate that the proposed method can significantly improve the performance in terms of both peak signal-to noise ratio (PSNR) and visual quality, while maintain a low computation.

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Acknowledgement

This work was supported by the National Nature Science Foundation of China (No. 61203269, No. 61305015), Postdoctoral Science Foundation of China (No. 2015M580591). Yun-Xia LIU acknowledges the research scholarships provided by the Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering and the Hong Kong Polytechnic University where the work is partially done.

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, China

    Yun-Xia Liu

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China

    Yun-Xia Liu

  3. School of Control Science and Engineering, Shandong University, Jinan, China

    Yun-Xia Liu

  4. School of Information Science and Engineering, Shandong University, Jinan, China

    Yang Yang

  5. Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Ngai-Fong Law

Authors
  1. Yun-Xia Liu
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  2. Yang Yang
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  3. Ngai-Fong Law
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Corresponding author

Correspondence to Yun-Xia Liu .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. University of Ulsan , Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Liu, YX., Yang, Y., Law, NF. (2016). Accurate Prior Modeling in the Locally Adaptive Window-Based Wavelet Denoising. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_47

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  • DOI: https://doi.org/10.1007/978-3-319-42294-7_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42293-0

  • Online ISBN: 978-3-319-42294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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