|  Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In Proc. the 6th Int. Conf. Computer Vision, January 1998, pp.839-846. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence, 1990, 12(7):629-639. 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. 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. Donoho D L. De-noising by soft-thresholding. IEEE Trans. Information Theory, 1995, 41(3):613-627. Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing, 2000, 9(9):1532-1546. Starck J L, Candes E J, Donoho D L. The curvelet transform for image denoising. IEEE Trans. Image Processing, 2002, 11(6):670-684. Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing, 2006, 15(12):3736-3745. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553):436-444. 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. Li H M. Deep learning for image denoising. Int. Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(3):171-180. 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. 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. Xie X L, Beni G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence, 1991, 13(8):841-847. Gersho A. On the structure of vector quantizers. IEEE Trans. Information Theory, 1982, 28(2):157-166. Chen Q, Wu D P. Image denoising by bounded block matching and 3D filtering. Signal Processing, 2010, 90(9):2778-2783. Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans. Computers, 1974, C-23(1):90-93. 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. Harris F J. On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. the IEEE, 1978, 66(1):51-83. 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. Schmitt J M, Xiang S H, Yung K M. Speckle in optical coherence tomography. Journal of Biomedical Optics, 1999, 4(1):95-105. 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.