Skip to main content
SpringerLink
Log in

IBM3D: Integer BM3D for Efficient Image Denoising

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

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.

References

  1. J. Bai, X.C. Feng, Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process. 16(10), 2492–2502 (2007)

    Article  MathSciNet  Google Scholar 

  2. A. Buades, B. Coll, J.M. Morel, A non-local algorithm for image denoising. CVPR 2, 60–65 (2005)

    MATH  Google Scholar 

  3. M. Budagavi, A. Fuldseth, G. Bjøntegaard, V. Sze, Core transform design in the high efficiency video coding (HEVC) standard. IEEE J. Sel. Top. Signal Process. 7(6), 1029–1041 (2013)

    Article  Google Scholar 

  4. H.C. Burger, C.J. Schuler, S. Harmeling, Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds. arXiv preprint arXiv:1211.1544 (2012)

  5. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  6. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image restoration by sparse 3D transform-domain collaborative filtering, in Electronic Imaging, International Society for Optics and Photonics, pp. 2080–2095 (2008)

  7. W. Dong, X. Li, D. Zhang, G. Shi, Sparsity-based image denoising via dictionary learning and structural clustering, in CVPR, pp. 457–464 (2011)

  8. W. Dong, L. Zhang, G. Shi, X. Li, Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  9. D.L. Donoho, Smooth wavelet decompositions with blocky coefficient kernels, in Recent Advances in Wavelet Analysis, pp. 1–43 (1993)

  10. M. Lebrun, An analysis and implementation of the BM3D image denoising method. Image Process. On Line 2(25), 175–213 (2012)

    Article  Google Scholar 

  11. L.I. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms. Physica D 60(1), 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  12. G.J. Sullivan, J. Ohm, W.J. Han, T. Wiegand, Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012)

    Article  Google Scholar 

  13. C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in ICCV, pp. 839–846 (1998)

  14. Z. Wang, R. Hu, G. Tian, M. Li, The generic generating algorithm for integer DCT transform radix. J Image Graph. 6, 007 (2008)

    Google Scholar 

  15. T. Wiegand, G.J. Sullivan, G. Bjontegaard, A. Luthra, Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13(7), 560–576 (2003)

    Article  Google Scholar 

  16. H. Zhang, W. Liu, R. Wang, T. Liu, M. Rong, Hardware architecture design of block-matching and 3D-filtering denoising algorithm. J. Shanghai Jiaotong Univ. (Sci.) 21(2), 173–183 (2016)

    Article  Google Scholar 

  17. W. Zuo, L. Zhang, C. Song, D. Zhang, Texture enhanced image denoising via gradient histogram preservation, in CVPR, pp. 1203–1210 (2013)

Download references

Author information

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

Authors
  1. Jingyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huanjing Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changrui Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chunping Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation