[1] Candès E J. Compressive sampling[C]//Proceedings of the International Congress of Mathe-maticians, 2006:1433-1452. [2] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. [3] Liu Y P, Shan W, Huang X L, et al. Hybrid CS-DMRI:periodic time-variant subsampling and omnidirectional total variation based reconstruction[J]. IEEE Transaction on Medical Imaging, 2017, 36(10):2148-2159. [4] Yuan X, Brady D J, Katsaggelos A K. Snapshot compressive imaging:theory, algorithms, and applications[J]. IEEE Signal Processing Magazine, 2021, 38(2):65-88. [5] Li C B, Yin W, Zhang Y. User's guide for TVAL3:TV minimization by augmented Lagrangian and alternating direction algorithms[J]. Computer Science, 2010, 20(46/47):4. [6] Zhang J, Zhao D B, Gao W. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8):3336-3351. [7] Chen C, Tramel E W, Fowler J E. Compressed-sensing recovery of images and video using multihypothesis predictions[C]//2011 Conference Record of the Forty-Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011:1193-1198. [8] Metzler C A, Maleki A, Baraniuk R G. From denoising to compressed sensing[J]. IEEE Transactions on Information Theory, 2016, 62(9):5117-5144. [9] Mousavi A, Patel A B, Baraniuk R G. A deep learning approach to structured signal recovery[C]//201553rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015:1336-1343. [10] Kulkarni K, Lohit S, Turaga P, et al. Reconnet:non-iterative reconstruction of images from compressively sensed measurements[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:449-458. [11] Yao H T, Dai F, Zhang S L, et al. DR2-Net:deep residual reconstruction network for image compressive sensing[J]. Neurocomputing, 2019, 359:483-493. [12] Zhang J, Ghanem B. ISTA-Net:interpretable optimization-inspired deep network for image compressive sensing[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:1828-1837. [13] Shi W Z, Jiang F, Zhang S P, et al. Deep networks for compressed image sensing[C]//2017 IEEE International Conference on Multimedia and Expo (ICME), 2017:877-882. [14] Shi W Z, Jiang F, Liu S H, et al. Image compressed sensing using convolutional neural network[J]. IEEE Transaction Image Process, 2019, 29:375-388. [15] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778. [16] Baraniuk R, Davenport M, Devore R, et al. A simple proof of the restricted isometry property for random matrices[J]. Constructive Approximation, 2008, 28(3):253-263. [17] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:7132-7141. [18] Zhang Y L, Li K P, Li K, et al. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018:286-301. [19] Timofte R, Agustsson E, Gool V L, et al. Ntire 2017 challenge on single image superresolution:methods and results[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017:114-125. [20] Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//The 23rd British Machine Vision Conference, 2012:135.1-135.10. [21] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[C]//International Conference on Curves and Surfaces, 2010:711-730. [22] Martin D, Fowlkes C, Tal D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]//Proceedings Eighth IEEE International Conference on Computer Vision, 2001:416-423. |