Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 838–848 | Cite as

3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

  • Bei-Ji Zou
  • Yun-Di Guo
  • Qi He
  • Ping-Bo Ouyang
  • Ke Liu
  • Zai-Liang ChenEmail author
Regular Paper


Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ > 40), and the best visual quality when denoising images with all the tested noise levels.


block matching convolutional neural network (CNN) denoising 3D filtering 


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Supplementary material

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  1. [1]
    Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In Proc. the 6th Int. Conf. Computer Vision, January 1998, pp.839-846.Google Scholar
  2. [2]
    Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.Google Scholar
  3. [3]
    Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 1992, 60(1/2/3/4): 259-268.Google Scholar
  4. [4]
    Osher S, Burger M, Goldfarb D, Xu J J, Yin W T. An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation, 2005, 4(2): 460-489.MathSciNetCrossRefzbMATHGoogle Scholar
  5. [5]
    Donoho D L. De-noising by soft-thresholding. IEEE Trans. Information Theory, 1995, 41(3): 613-627.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing, 2000, 9(9): 1532-1546.MathSciNetCrossRefzbMATHGoogle Scholar
  7. [7]
    Starck J L, Candes E J, Donoho D L. The curvelet transform for image denoising. IEEE Trans. Image Processing, 2002, 11(6): 670-684.MathSciNetCrossRefzbMATHGoogle Scholar
  8. [8]
    Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing, 2006, 15(12): 3736-3745.MathSciNetCrossRefGoogle Scholar
  9. [9]
    Dong W S, Zhang L, Shi G M, Li X. Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Processing, 2013, 22(4): 1620-1630.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, June 2005, pp.60-65.Google Scholar
  11. [11]
    Gu S H, Zhang L, Zuo W M, Feng X C. Weighted nuclear norm minimization with application to image denoising. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2014.Google Scholar
  12. [12]
    Jain V, Seung H S. Natural image denoising with convolutional networks. In Proc. the 21st Int. Conf. Neural Information Processing Systems, December 2008, pp.769-776.Google Scholar
  13. [13]
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P A. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 2010, 11: 3371-3408.MathSciNetzbMATHGoogle Scholar
  14. [14]
    Xie J Y, Xu L L, Chen E H. Image denoising and inpainting with deep neural networks. In Proc. the 25th Int. Conf. Neural Information Processing Systems, December 2012, pp.341-349.Google Scholar
  15. [15]
    Vemulapalli R, Tuzel O, Liu M Y. Deep Gaussian conditional random field network: A model-based deep network for discriminative denoising. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2016.Google Scholar
  16. [16]
    Zhang K, Zuo W M, Chen Y J, Meng D Y, Zhang L. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Processing, 2017, 26(7): 3142-3155.MathSciNetCrossRefGoogle Scholar
  17. [17]
    Peyré G, Bougleux S, Cohen L. Non-local regularization of inverse problems. In Proc. the 10th European Conf. Computer Vision, October 2008, pp.57-68.Google Scholar
  18. [18]
    Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Processing, 2007, 16(8): 2080-2095.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Zhang L, Dong W S, Zhang D, Shi G M. Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition, 2010, 43(4): 1531-1549.CrossRefzbMATHGoogle Scholar
  20. [20]
    Dong W S, Li X, Zhang L, Shi G M. Sparsity-based image denoising via dictionary learning and structural clustering. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2011, pp.457-464.Google Scholar
  21. [21]
    Liu H F, Xiong R Q, Zhang J, Gao W. Image denoising via adaptive soft-thresholding based on non-local samples. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2015, pp.484-492.Google Scholar
  22. [22]
    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.CrossRefGoogle Scholar
  23. [23]
    Burger H C, Schuler C J, Harmeling S. Image denoising: Can plain neural networks compete with BM3D? In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.2392-2399.Google Scholar
  24. [24]
    Li H M. Deep learning for image denoising. Int. Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3): 171-180.Google Scholar
  25. [25]
    Agostinelli F, Anderson M R, Lee H. Adaptive multicolumn deep neural networks with application to robust image denoising. In Proc. the 26th Int. Conf. Neural Information Processing Systems, December 2013, pp.1493-1501.Google Scholar
  26. [26]
    MacQueen J. Some methods for classification and analysis of multivariate observations. In Proc. the 5th Berkeley Symp. Mathematical Statistics and Probability, June 1967, pp.281-297.Google Scholar
  27. [27]
    Xie X L, Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847.Google Scholar
  28. [28]
    Gersho A. On the structure of vector quantizers. IEEE Trans. Information Theory, 1982, 28(2): 157-166.MathSciNetCrossRefGoogle Scholar
  29. [29]
    Chen Q,Wu D P. Image denoising by bounded block matching and 3D filtering. Signal Processing, 2010, 90(9): 2778-2783.CrossRefzbMATHGoogle Scholar
  30. [30]
    Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans. Computers, 1974, C-23(1): 90-93.MathSciNetCrossRefzbMATHGoogle Scholar
  31. [31]
    Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. In Proc. the 27th Int. Conf. Machine Learning, June 2010, pp.807-814.Google Scholar
  32. [32]
    Harris F J. On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. the IEEE, 1978, 66(1): 51-83.CrossRefGoogle Scholar
  33. [33]
    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Processing, 2004, 13(4): 600-612.CrossRefGoogle Scholar
  34. [34]
    Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography. Journal of Biomedical Optics, 1999, 4(1): 95-105.CrossRefGoogle Scholar
  35. [35]
    Fang L Y, Li S T, Nie Q, Izatt J A, Toth C A, Farsiu S. Sparsity based denoising of spectral domain optical coherence tomography images. Biomedical Optics Express, 2012, 3(5): 927-942.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Bei-Ji Zou
    • 1
    • 2
  • Yun-Di Guo
    • 1
    • 3
  • Qi He
    • 1
    • 3
  • Ping-Bo Ouyang
    • 1
    • 3
  • Ke Liu
    • 2
  • Zai-Liang Chen
    • 1
    • 3
    Email author
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Center for Information and Automation of China Nonferrous Metals Industry AssociationChangshaChina
  3. 3.Center for Ophthalmic Imaging ResearchCentral South UniversityChangshaChina

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