Circuits, Systems, and Signal Processing

, Volume 38, Issue 2, pp 750–763 | Cite as

IBM3D: Integer BM3D for Efficient Image Denoising

  • Jingyu Yang
  • Xue Zhang
  • Huanjing YueEmail author
  • Changrui Cai
  • Chunping Hou


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.


Image denoising Integer implementation DWT DCT Wiener filtering 


  1. 1.
    J. Bai, X.C. Feng, Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process. 16(10), 2492–2502 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    A. Buades, B. Coll, J.M. Morel, A non-local algorithm for image denoising. CVPR 2, 60–65 (2005)zbMATHGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)MathSciNetCrossRefGoogle Scholar
  6. 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)Google Scholar
  7. 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)Google Scholar
  8. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    D.L. Donoho, Smooth wavelet decompositions with blocky coefficient kernels, in Recent Advances in Wavelet Analysis, pp. 1–43 (1993)Google Scholar
  10. 10.
    M. Lebrun, An analysis and implementation of the BM3D image denoising method. Image Process. On Line 2(25), 175–213 (2012)CrossRefGoogle Scholar
  11. 11.
    L.I. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms. Physica D 60(1), 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in ICCV, pp. 839–846 (1998)Google Scholar
  14. 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. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 17.
    W. Zuo, L. Zhang, C. Song, D. Zhang, Texture enhanced image denoising via gradient histogram preservation, in CVPR, pp. 1203–1210 (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

Personalised recommendations