[1] [1] FROST V SSTILES J ASHANMUGAN K Set al.A model for radar images and its application to adaptive digital filtering of multiplicative noise ［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence1982PAMI-4(2):157-166.

[3] [3] KUAN D TSAWCHUK A ASTRAND T Cet al.Adaptive noise smoothing filter for images with signal-dependent noise［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence1985PAMI-7(2):165-177.

[4] [4] DONOHO D.L.De-noising by soft-thresholding［J］.IEEE Transactions on Information Theory199541(3):613-627.

[5] [5] JOY JPETER SJOHN N.Denoising using soft thresholding［J］.International Journal of Advanced Research in Electrical Electronics & Instrumentation Engineering 20132(3): 1027-1032.

[6] [6] DABOV KFOI AKATKOVNIK Vet al.Image restoration by sparse 3D transform-domain collaborative filtering ［M］//ASTOLA J TEGIAZARIAN K ODOUGHERTY E R.Image processing:algorithms and systems vi.Bellingham: SPIE(International Society for Optical Engineering), 2008.

[8] [8] CHENG GLI Z PHAN J Wet al.Exploring hierarchical convolutional features for hyperspectral image classification ［J］.IEEE Transactions on Geoscience and Remote Sensing201856(11):6712-6722.

[9] [9] LEE S LZARE M R.Biomedical compound figure detection using deep learning and fusion techniques ［J］.IET Image Processing201812(6):1031-1037.

[10] [10] CHANG XJIAO L CLIU Fet al.SAR image despeckling using scale mixtures of Gaussians in the nonsubsampled contourlet domain ［J］.Chinese Journal of Electronics201524(1):205-211.

[11] [11] CAO X HJI Y MWANG Let al.SAR image change detection based on deep denoising and CNN ［J］.IET Image Processing201913(9):1509-1515.

[12] [12] GOODFELLOW I JPOUGET-ABADIE JMIRZA Met al.Generative adversarial nets［M］//GHAHRAMANI ZWELLING MCORTES Cet al.Advances in neural information processing systems 27.La Jolla:Neural Information Processing Systems (NIPS),2014:2672-2680.

[14] [14] RADFORD AMETZ LCHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks［C］//ICLR2016.doi:10.48550/arXiv.1511.06434.

[15] [15] HE K MZHANG X YREN S Qet al.Deep residual learning for image recognition［C］//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las VegasNV:IEEE2016.doi:10.1109/CVPR.2016.90.

[17] [17] WANG P YZHANG HPATEL V M.Generative adversarial network-based restoration of speckled SAR images［C］//IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).Curacao:IEEE2018.doi:10.1109/CAMSAP.2017.8313159.

[18] [18] WU QFAN C XLI Yet al.A novel perceptual loss function for single image super-resolution［J］.Multimedia Tools and Applications202079(29):21265-21278.

[22] [22] HOR AZIOU D.Image quality metrics:PSNR vs.SSIM［C］//The 20th International Conference on Pattern Recognition.Istanbul:IEEE2010:2366-2369.