AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper

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

School of Information Science and Engineering, Central South University, Changsha 410083, China
Center for Information and Automation of China Nonferrous Metals Industry Association, Changsha 410011, China
Center for Ophthalmic Imaging Research, Central South University, Changsha 410083, China

Recommended by ICPCSEE 2017

Show Author Information

Abstract

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.

Keywords

convolutional neural network (CNN)denoisingblock matching3D filtering

Electronic Supplementary Material

Download File(s)
jcst-33-4-838-Highlights.pdf (52.3 KB)

References

[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[5]

Donoho D L. De-noising by soft-thresholding. IEEE Trans. Information Theory, 1995, 41(3): 613-627.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[8]

Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing, 2006, 15(12): 3736-3745.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref
[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.
Crossref
[22]

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
Crossref Google Scholar
[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.
Crossref
[24]

Li H M. Deep learning for image denoising. Int. Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3): 171-180.
Crossref Google Scholar
[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.
Crossref Google Scholar
[28]

Gersho A. On the structure of vector quantizers. IEEE Trans. Information Theory, 1982, 28(2): 157-166.
Crossref Google Scholar
[29]

Chen Q,Wu D P. Image denoising by bounded block matching and 3D filtering. Signal Processing, 2010, 90(9): 2778-2783.
Crossref Google Scholar
[30]

Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans. Computers, 1974, C-23(1): 90-93.
Crossref Google Scholar
[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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[34]

Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography. Journal of Biomedical Optics, 1999, 4(1): 95-105.
Crossref Google Scholar
[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.
Crossref Google Scholar
Journal of Computer Science and Technology
Volume 33 Issue 4,
July 2018
Pages 838-848
DOI: 10.1007/s11390-018-1859-7
Cite this article:
Zou B-J, Guo Y-D, He Q, et al. 3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising. Journal of Computer Science and Technology, 2018, 33(4): 838-848. https://doi.org/10.1007/s11390-018-1859-7

368

Views

19

Crossref

N/A

Web of Science

17

Scopus

0

CSCD

Altmetrics
Received: 22 June 2017
Revised: 26 January 2018
Published: 13 July 2018
©2018 LLC & Science Press, China
Return