[1] [1] Povazay B, Bizheva K, Unterhuber A, et al. Submicrometer axial resolution optical coherence tomography［J］. Optics Letters,2002,27(20):1800-1802.

[2] [2] Huang D, Swanson E A, Lin C P, et al. Optical coherence tomography［J］. Science,1991,254(5035):1178-1181.

[3] [3] Buades A, Coll B, Morel J M. Nonlocal image and movie denoising［J］. International Journal of Computer Vision,2008,76(2):123-139.

[4] [4] Iftimia N, Bouma B E, Tearney G J. Speckle reduction in optical coherence tomography by［J］. Journal of Biomedical Optics,2003,8(2):260-263.

[5] [5] Chong B, Zhu Y K. Speckle reduction in optical coherence tomography images of human finger skin by wavelet modified BM3D filter［J］. Optics Communications,2013,291:461-469.

[6] [6] Salinas H M, Fernández D C. Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography［J］. IEEE Transactions on Medical Imaging,2007,26(6):761-771.

[7] [7] Puvanathasan P, Bizheva K. Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images［J］. Optics Express,2009,17(2):733-746.

[9] [9] Mao X, Shen C, Yang Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections［J］. Advances in Neural Information Processing Systems,2016,29:2802-2810.

[10] [10] Zhang K, Zuo W, Chen Y, et al. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising［J］. IEEE Transactions on Image Processing,2017,26(7):3142-3155.

[11] [11] Tai Y, Yang J, Liu X, et al. Memnet: A persistent memory network for image restoration［C］∥Proceedings of The IEEE International Conference on Computer Vision. Shenzhen,China:IEEE,2017: 4539-4547.

[12] [12] Ma Y, Chen X, Zhu W, et al. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN［J］. Biomedical Optics Express,2018,9(11):5129-5146.

[13] [13] Ioffe S, Szegedy C. Batch Normalization: Accelerating deep network training by reducing internal covariate shift［J］. arXiv preprint arXiv: 2015,1502:03167.

[14] [14] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［J］. arXiv Preprint arXiv: 2014,1409:1556.

[15] [15] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston, USA:IEEE,2015:1-9.8.

[16] [16] Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network［C］∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,USA:IEEE,2017:2881-2890.

[17] [17] Huang Z, Wang X, Huang L, et al. Ccnet: Criss-cross attention for semantic segmentation［C］∥Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea:IEEE,2019:603-612.

[18] [18] Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering［J］. IEEE Transactions on Image Processing,2007,16(8):2080-2095.

[19] [19] 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. San Diego, USA:IEEE,2005,2:60-65.

[20] [20] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition［C］∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE,2016:770-778.

[21] [21] Chen Y, Pock T. Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(6):1256-1272.

[22] [22] Burger H C, Schuler C J, Harmeling S. Image denoising: Can plain neural networks compete with BM3D［C］∥2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA:IEEE,2012:2392-2399.

[23] [23] Yang B, Bender G, Le Q V, et al. Condconv: Conditionally parameterized convolutions for efficient inference［J］. arXiv preprint arXiv: 2019,1904:04971.

[24] [24] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks［C］∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. Honolulu,USA:IEEE,2017:4700-4708.

[25] [25] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.

[26] [26] Bogunovic H, Venhuizen F, Klimscha S, et al. RETOUCH-The retinal OCT fluid detection and segmentation benchmark and challenge［J］. IEEE Transactions on Medical Imaging,2019,(99):1-1.

[27] [27] Kingma D P, Ba J. Adam: A method for stochastic optimization［J］. arXiv preprint arXiv: 2014,1412:6980.