›› 2018, Vol. 33 ›› Issue (4): 838-848.doi: 10.1007/s11390-018-1859-7

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

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

Bei-Ji Zou1,2, Yun-Di Guo1,3, Qi He1,3, Ping-Bo Ouyang1,3, Ke Liu2, Zai-Liang Chen1,3,*   

  1. 1 School of Information Science and Engineering, Central South University, Changsha 410083, China;
    2 Center for Information and Automation of China Nonferrous Metals Industry Association, Changsha 410011, China;
    3 Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China
  • Received:2017-06-22 Revised:2018-01-26 Online:2018-07-05 Published:2018-07-05
  • Contact: Zai-Liang Chen,E-mail:zailiangchencs@gmail.com E-mail:zailiangchencs@gmail.com
  • About author:Bei-Ji Zou received his B.S. degree in computer software from Zhejiang University, Hangzhou, in 1982, and his M.S. and Ph.D. degrees in computer science and technology from Tsinghua University, Beijing, in 1984, and Hunan University, Changsha, in 2001, respectively. He joined the School of Computer and Communication at Hunan University, Changsha, in 1984, where he became an associate professor in 1997, and a professor in 2001. He served as the vice dean there since 1997. He is currently a professor and served as the dean of the School of Information Science and Engineering in Central South University, Changsha. His research interest is focused on computer graphics, image processing, and virtual reality technology. Until now he has published more than 100 papers in journals.
  • Supported by:

    This research was supported by the National Natural Science Foundation of China under Grant Nos. 61573380 and 61672542, and Fundamental Research Funds for the Central Universities of China under Grant No. 2016zzts055.

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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.

[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.

[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.

[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.

[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.

[5] Donoho D L. De-noising by soft-thresholding. IEEE Trans. Information Theory, 1995, 41(3):613-627.

[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.

[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.

[8] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing, 2006, 15(12):3736-3745.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[22] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553):436-444.

[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.

[24] Li H M. Deep learning for image denoising. Int. Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3):171-180.

[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.

[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.

[27] Xie X L, Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence, 1991, 13(8):841-847.

[28] Gersho A. On the structure of vector quantizers. IEEE Trans. Information Theory, 1982, 28(2):157-166.

[29] Chen Q, Wu D P. Image denoising by bounded block matching and 3D filtering. Signal Processing, 2010, 90(9):2778-2783.

[30] Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans. Computers, 1974, C-23(1):90-93.

[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.

[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.

[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.

[34] Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography. Journal of Biomedical Optics, 1999, 4(1):95-105.

[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.
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[2] Li Wei;. A Structural Operational Semantics for an Edison Like Language(2)[J]. , 1986, 1(2): 42 -53 .
[3] Li Wanxue;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[4] Feng Yulin;. Recursive Implementation of VLSI Circuits[J]. , 1986, 1(2): 72 -82 .
[5] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[6] Sun Zhongxiu; Shang Lujun;. DMODULA:A Distributed Programming Language[J]. , 1986, 1(2): 25 -31 .
[7] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[8] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
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