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Affine non-local Bayesian image denoising algorithm

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Abstract

This paper proposes an extension of the non-local Bayesian denoising algorithm. The idea is to use elliptical patches instead of regular square patches in the grouping process. We calculate the elliptical patches by an iterative method. We then use an affine invariant patch similarity measure to calculate the distance between two elliptical patches. Since the elliptical patch is a shape-adaptive patch and this similarity measure performs a patch comparison by automatically adapting the size and shape of the patches, so more similar patches are found and used for image denoising. This algorithm denoising procedure goes through two identical iterations to further improve the denoising performance. Experimental results on test images demonstrate that this algorithm achieves state-of-the-art denoising performance in terms of numerical results and subjective visual quality, compared with the non-local Bayesian.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 12171054, and the fund of the "Thirteen Five" Scientific and Technological Research Planning Project of the Department of Education of Jilin Province (JJKH20200726KJ). The authors would like to thank the anonymous reviewers and editor for their helpful feedback and suggestions.

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Authors and Affiliations

  1. Department of Mathematics, School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, China

    Huaping Xu, Xiaoning Jia & Libo Cheng

  2. College of Sciences, Shanghai Institute of Technology, Shanghai, China

    Heyan Huang

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  1. Huaping Xu
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  2. Xiaoning Jia
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  3. Libo Cheng
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  4. Heyan Huang
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Correspondence to Xiaoning Jia.

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Xu, H., Jia, X., Cheng, L. et al. Affine non-local Bayesian image denoising algorithm. Vis Comput 39, 99–118 (2023). https://doi.org/10.1007/s00371-021-02316-x

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