[1] Zhang H, Chi Y, Zhou Y T et al. Three dimensional seismic signal denoising based on four-dimensional block matching cooperative filtering combined with principle component analysis[J]. Laser & Optoelectronics Progress, 55, 041007(2018).

[2] Zhang M, Lv X Q, Wu L et al. Multiplicative denoising method based on deep residual learning[J]. Laser & Optoelectronics Progress, 55, 031004(2018).

[3] Ni X, Lü Q W, Meng F et al. Image denoising method based on curvelet transform and total variation[J]. Acta Optica Sinica, 29, 2390-2394(2009).

[4] Yu S, Cai X, Su Y. Seismic signal enhancement by polynomial fitting[J]. Applied Geophysics, 1, 57-65(1989).

[5] Zhu W H, Kelamis P G, Liu Q L. Linear noise attenuation using local radial trace median filtering[J]. The Leading Edge, 23, 728-737(2004). http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=LEEDFF000023000008000728000001&idtype=cvips&prog=normal

[6] Li Y J, Yang B J, Li Y et al. Combining SVD with wavelet transform in synthetic seismic signal denoising. [C]∥Proceedings of 2007 International Conference on Wavelet Analysis and Pattern Recognition, 4, 1831-1836(2007).

[7] Tang G, Ma J W, Yang H Z. Seismic data denoising based on learning-type overcomplete dictionaries[J]. Applied Geophysics, 9, 27-32(2012). http://d.wanfangdata.com.cn/Periodical/yydqwl201201004

[8] Zhang G Z, Chang D K, Wang Y H et al. 3D seismic random noise suppression with sparse and redundant representation[J]. Oil Geophysical Prospecting, 50, 600-606(2015).

[9] Tang J Y, Chen W T, Chen S Y et al. Application of wavelet-based denoising using a new adaptive thresholding function to vibration signals[J]. Journal of Vibration and Shock, 28, 118-121(2009).

[10] Wang H Q, Shang C Y, Gao R P et al. An improvement of wavelet shrinkage denoising via wavelet coefficient transformation[J]. Journal of Vibration and Shock, 30, 165-168(2011).

[11] Chen G Y, Kégl B. Image denoising with complex ridgelets[J]. Pattern Recognition, 40, 578-585(2007). http://www.sciencedirect.com/science/article/pii/S0031320306001816

[12] Ji J, Xu S X, Li X. An adaptive thresholding image denoising method based on morphological component analysis and contourlet transform[J]. Pattern Recognition and Artificial Intelligence, 27, 561-568(2014).

[13] Bhadauria H S, Dewal M L. Medical image denoising using adaptive fusion of curvelet transform and total variation[J]. Computers & Electrical Engineering, 39, 1451-1460(2013). http://dl.acm.org/citation.cfm?id=2501381

[14] Yang N, He Z H, Huang D J. Signal and noise separation method for pre-stack seismic data in wave atomic domain based on coefficient correlation threshold[J]. Oil Geophysical Prospecting, 46, 53-57(2011).

[15] Elad M, Starck J L, Querre P et al. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)[J]. Applied and Computational Harmonic Analysis, 19, 340-358(2005). http://www.sciencedirect.com/science/article/pii/S1063520305000655

[16] Mairal J, Bach F, Ponce J et al. Non-local sparse models for image restoration. [C]∥Proceedings of 2009 IEEE 12 ^th International Conference on Computer Vision , 2272-2279(2009).

[17] Mairal J, Sapiro G, Elad M. Learning multiscale sparse representations for imageand video restoration[J]. Multiscale Modeling & Simulation, 7, 214-241(2008).

[18] Quan Y L, Zhu T Y, Harris J M et al. Image integration with learned dictionaries and application to seismic monitoring. [C]∥Proceedings of SEG Technical Program Expanded Abstracts, 30, 4217-4222(2011).

[19] Cai J F, Ji H, Shen Z W et al. Data-driven tight frame construction and image denoising[J]. Applied and Computational Harmonic Analysis, 37, 89-105(2014). http://www.sciencedirect.com/science/article/pii/S1063520313000924

[20] Protter M, Elad M. Image sequence denoising via sparse and redundant representations[J]. IEEE Transactions on Image Processing, 18, 27-35(2009). http://dl.acm.org/citation.cfm?id=1657203

[21] Bryt O, Elad M. Compression of facial images using the K-SVD algorithm[J]. Journal of Visual Communication and Image Representation, 19, 270-282(2008). http://dl.acm.org/citation.cfm?id=1365228

[22] Zibulevsky M, Pearlmutter B A. Blind source separation by sparse decomposition in a signal dictionary[J]. Neural Computation, 13, 863-882(2001). http://europepmc.org/abstract/med/11255573

[23] Sahoo S K, Makur A. Dictionary training for sparse representation as generalization of K-means clustering[J]. IEEE Signal Processing Letters, 20, 587-590(2013).

[24] Sahoo S K, Makur A. Enhancing image denoising by controlling noise incursion in learned dictionaries[J]. IEEE Signal Processing Letters, 22, 1123-1126(2015). http://ieeexplore.ieee.org/document/7004012/

[25] Chen Y K. Fast dictionary learning for noise attenuation of multidimensional seismic data[J]. Geophysical Journal International, 209, 21-31(2017). http://gji.oxfordjournals.org/content/early/2017/01/11/gji.ggw492.abstract

[26] Hennenfent G, Herrmann F J. Simply denoise:wavefield reconstruction via jittered undersampling[J]. Geophysics, 73, V19-V28(2008). http://adsabs.harvard.edu/abs/2008Geop...73...19H

[27] Herrmann F J, Hennenfent G. Non-parametric seismic data recovery with curvelet frames[J]. Geophysical Journal International, 173, 233-248(2008). http://onlinelibrary.wiley.com/doi/10.1111/j.1365-246X.2007.03698.x/pdf