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IBM3D: Integer BM3D for Efficient Image Denoising

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Abstract

The block-matching collaborative filtering (BM3D) denoiser has been considered as a strong performer in image denoising, but it has high computational cost in block-matching and 3D transforms, which limits its practical applications, particularly in embedded video processing systems. In this paper, we propose an integer BM3D (IBM3D) that involves only integer operations. To integerize 3D transforms, the balance of approximation accuracy and denoising performance is carefully investigated for a wide range of noise levels. We propose an integer Wiener filter and investigate its performance over the original empirical Wiener filter with both analytical analysis and experimental verifications. The Kaiser window weighting is also integerized. The experiment results show that the proposed IBM3D provides comparable denoising performance to the original BM3D, and generates even better results for high noise levels. The proposed IBM3D requires less computation than the original BM3D, and can be deployed into embedded systems without or with limited floating-point computation resources, and ported to chips with smaller circuit areas and less power consumption.

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Notes

  1. Note that the implementation in [10] is slower than the original BM3D released by [5], since [5] utilizes advanced accelerating method whose details are not publicly available.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Jingyu Yang, Xue Zhang, Huanjing Yue, Changrui Cai & Chunping Hou

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  1. Jingyu Yang
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  2. Xue Zhang
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  3. Huanjing Yue
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  4. Changrui Cai
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  5. Chunping Hou
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Corresponding author

Correspondence to Huanjing Yue.

Additional information

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61771339, 61672378, and 61520106002, and in part by the Elite Scholar Program of Tianjin University.

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Yang, J., Zhang, X., Yue, H. et al. IBM3D: Integer BM3D for Efficient Image Denoising. Circuits Syst Signal Process 38, 750–763 (2019). https://doi.org/10.1007/s00034-018-0882-9

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  • DOI: https://doi.org/10.1007/s00034-018-0882-9

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