[1] SHANNON C E. Communication in the presence of noise[J]. Proceedings of the IRE, 37, 10-21(1949).

[2] CANDÈS E J, WAKIN M B, BOYD S P. Enhancing sparsity by reweighted ℓ[J]. Journal of Fourier Analysis and Applications, 14, 877-905(2008).

[3] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

[4] BARANIUK R, CANDES E, NOWAK R et al. Compressive sampling[J]. IEEE Signal Processing Magazine, 25, 12-13(2008).

[5] SHI W Z, LIU S H, JIANG F et al. Video compressed sensing using a convolutional neural network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 31, 425-438(2021).

[6] ROGERS C A, POPESCU D C. Compressed sensing MIMO radar system for extended target detection[J]. IEEE Systems Journal, 15, 1381-1389(2021).

[7] [7] 7黄博， 周劼， 江舸. 稳健型双层叠组LASSO逆合成孔径雷达高分辨成像算法［J］. 电子与信息学报， 2021， 43（3）： 674-682. doi: 10.11999/JEIT200338HUANGB， ZHOUJ， JIANGG. High resolution ISAR imaging algorithm based on robust two-tier group LASSO alternating direction method of multipliers［J］. Journal of Electronics & Information Technology， 2021， 43（3）： 674-682.（in Chinese）. doi: 10.11999/JEIT200338

[8] KUMAR P A, GUNASUNDARI R, AARTHI R. Systematic analysis and review of magnetic resonance imaging （MRI） reconstruction techniques[J]. Current Medical Imaging Formerly Current Medical Imaging Reviews, 17, 943-955(2021).

[9] SHI W Z, JIANG F, LIU S H et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 29, 375-388(2020).

[10] MALLAT S G, ZHANG Z F. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 41, 3397-3415(1993).

[11] TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 53, 4655-4666(2007).

[12] FORNASIER M, RAUHUT H. Iterative thresholding algorithms[J]. Applied and Computational Harmonic Analysis, 25, 187-208(2008).

[13] BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2, 183-202(2009).

[14] WRIGHT S J, NOWAK R D, FIGUEIREDO M A T. Sparse reconstruction by separable approximation[J]. IEEE Transactions on Signal Processing, 57, 2479-2493(2009).

[15] FIGUEIREDO M A T, NOWAK R D, WRIGHT S J. Gradient projection for sparse reconstruction： application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 1, 586-597(2007).

[16] FOWLER J E. Block Compressed Sensing of Images Using Directional Transforms[C], 547(24).

[17] [17] 17景宁， 姚鼎一， 王志斌， 等. 等效时间采样压缩感知高频信号重建［J］. 光学 精密工程， 2022， 30（10）：1240-1245. doi: 10.37188/OPE.20223010.1240JINGN， YAOD Y， WANGZ B， et al. High frequency signal reconstruction based on compressive sensing and equivalent-time sampling［J］. Opt. Precision Eng.， 2022， 30（10）1240-1245.（in Chinese）. doi: 10.37188/OPE.20223010.1240

[18] KULKARNI K, LOHIT S, TURAGA P et al. ReconNet： Non-Iterative Reconstruction of Images From Compressively Sensed Measurements[C], 449-458(27).

[19] SHI W Z, JIANG F, ZHANG S P et al. Deep Networks for Compressed Image Sensing[C], 877-882(10).

[20] SHI W Z, JIANG F, LIU S H et al. Scalable Convolutional Neural Network for Image Compressed Sensing[C], 12282-12291(15).

[21] YANG Y, SUN J, LI H B et al. ADMM-CSNet： a deep learning approach for image compressive sensing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 521-538(2020).

[22] ZHANG J, ZHAO C, GAO W. Optimization-inspired compact deep compressive sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 14, 765-774(2020).

[23] CHEN C, TRAMEL E W, FOWLER J E. Compressed-Sensing Recovery of Images and Video Using Multihypothesis Predictions[C], 1193-1198(6).

[24] ZHANG J, GHANEM B. ISTA-net： Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing[C], 1828-1837(18).

[25] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[26] HE K M, ZHANG X Y, REN S Q et al. Deep Residual Learning for Image Recognition[C], 770-778(27).

[27] CHI L, JIANG B, MU Y. Fast fourier convolution[J]. Advances in Neural Information Processing Systems, 33, 4479-4488(2020).

[28] SEITZ S M, BAKER S. Filter Flow[C], 143-150(2009).

[29] SHI W Z, CABALLERO J, HUSZÁR F et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network[C], 1874-1883(27).

[30] YANG J C, WRIGHT J, HUANG T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 19, 2861-2873(2010).

[31] BEVILACQUA M, ROUMY A, GUILLEMOT C et al. Low-Complexity Single-Image Super-Resolution Based on Nonnegative Neighbor Embedding[C](2012).

[32] BO L, LU H C, LU Y J et al. FompNet： Compressive Sensing Reconstruction with Deep Learning over Wireless Fading Channels[C], 1-6(11).

[33] MARTIN D, FOWLKES C et al. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics[C], 416-423(14).

[34] WANG Z, BOVIK A C, SHEIKH H R et al. Image quality assessment： from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).