Full-Reference Metric Adaptive Image Denoising

Kenji Hara, Kohei Inoue, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)


    The performance of image denoising algorithms generally depends largely on the selection of the parameters. We address the problem of optimizing the denoising parameters to achieve maximum denoising performance. Most existing methods for no-reference denoising parameter optimization either use the estimated image noise or individual no-reference image quality evaluation metrics. In the paper, we introduce a natural image statistics based on the generalized Gaussian distribution and an elastic net regularization regression model. Consequently, we propose their use for performing no-reference parameter optimization in the state-of-the-art BM3D denoising algorithm, adaptively depending on which of the following most widely used full-reference image quality evaluation metrics is optimized: FSIM, SSIM, MS-SSIM, without any knowledge of the noise distribution.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
    PublisherIEEE Computer Society
    Number of pages5
    ISBN (Electronic)9781538662496
    Publication statusPublished - Sept 2019
    Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
    Duration: Sept 22 2019Sept 25 2019

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    ISSN (Print)1522-4880


    Conference26th IEEE International Conference on Image Processing, ICIP 2019
    Country/TerritoryTaiwan, Province of China

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing


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