[1] Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling & Simulation, 4, 490-530(2005). http://ci.nii.ac.jp/naid/10028228930

[2] Benesty J, Chen J D, Huang Y T. Study of the widely linear Wiener filter for noise reduction[C]. //2010 IEEE International Conference on Acoustics, Speech and Signal Processing, March 14-19, 2010, Dallas, TX, USA., 205-208(2010).

[3] Gavaskar R G, Chaudhury K N. Fast adaptive bilateral filtering[J]. IEEE Transactions on Image Processing, 28, 779-790(2019).

[4] He K, Sun J, Tang X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1397-1409(2013).

[5] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C]. //2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), June 20-25, 2005, San Diego, CA, USA., 60-65(2005).

[6] Komatsu T, Tyon K, Saito T. 3-D mean-separation-type short-time DFT with its application to moving-image denoising[C]. //2017 IEEE International Conference on Image Processing (ICIP), September 17-20, 2017, Beijing., 2961-2965(2017).

[7] Shen Y, Liu Q, Lou S Q et al. Wavelet-based total variation and nonlocal similarity model for image denoising[J]. IEEE Signal Processing Letters, 24, 877-881(2017). http://ieeexplore.ieee.org/document/7888486

[8] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 15, 3736-3745(2006). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/TIP.2006.881969&rfr_id=trans/tp/2009/04/ttp2009040693.htm

[9] Mairal J, Bach F, Ponce J et al. Non-local sparse models for image restoration[C]. //2009 IEEE 12th International Conference on Computer Vision, September 29-October 2, 2009, Kyoto, Japan., 2272-2279(2009).

[10] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007). http://www.ncbi.nlm.nih.gov/pubmed/17688213

[11] Yu L Q, Qin Y L, Zhang X S. Denoising of strong noisy image via gradient reweighted non-local averaging over learned dictionaries[J]. Laser & Optoelectronics Progress, 53, 111002(2016).

[12] Lu Y S, Li Y X, Liu B et al. Hyperspectral data haze monitoring based on deep residual network[J]. Acta Optica Sinica, 37, 1128001(2017).

[13] Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 37, 1210002(2017).

[14] Chen Q J, Li Y, Chai Y Z. A multi-focus image fusion algorithm based on depth learning[J]. Laser & Optoelectronics Progress, 55, 071015(2018).

[15] Jain V, Seung S. Natural image denoising with convolutional networks[C]. //Advances in Neural Information Processing Systems, December 8, 2008, Red Hook, NY, United States, 769-776(2008).

[16] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[17] Yuan K P, Xi Z H. Image super resolution based on depth jumping cascade[J]. Acta Optica Sinica, 39, 0715003(2019).

[18] Xi Z H, Hou C Y, Yuan K P et al. Super-resolution reconstruction of accelerated image based on deep residual network[J]. Acta Optica Sinica, 39, 0210003(2019).

[19] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

[20] Zhang K, Zuo W M, Gu S H et al. Learning deep CNN denoiser prior for image restoration[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2808-2817(2017).

[21] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2472-2481(2018).

[22] Zhang K, Zuo W M, Zhang L. FFDNet: toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 27, 4608-4622(2018). http://ieeexplore.ieee.org/document/8365806/

[23] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[24] Liu D, Wen B, Fan Y et al. Non-local recurrent network for image restoration[C]. //NIPS’18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, December 3, 2018, Red Hook, NY, United States, 1673-1682(2018).

[25] Liu X, Suganuma M, Sun Z et al. Dual residual networks leveraging the potential of paired operations for image restoration[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 7000-7009(2019).

[26] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA., 1132-1140(2017).

[27] Zhang Y L, Li K P, Li K et al. Residual non-local attention networks for image restoration[EB/OL]. (2019-03-24)[2020-10-10]. http://arxiv.org/abs/1903.10082

[28] Suganuma M, Ozay M, Okatani T. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search[EB/OL]. (2018-03-01)[2020-10-10]. https://arxiv.org/abs/1803.00370

[29] Wu L, Lü G Q, Xue Z T et al. Super-resolution reconstruction of images based on multi-scale recursive network[J]. Acta Optica Sinica, 39, 0610001(2019).

[30] Tong Y B, Zhang Q S, Qi Y P. Image quality assessing by combining PSNR with SSIM[J]. Journal of Image and Graphics, 11, 1758-1763(2006).