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Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e.
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Y. Liu, M. De Vos, I. Gligorijevic, V. Matic, Y. Li, and S. Van Huffel, “Multi-structural signal recovery for biomedical compressive sensing,” IEEE Transactions on Biomedical Engineering , vol. 60, no. 10, pp. 2794–2805, 2013
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C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented Lagrangian method with applications to total variation minimization,” Computational Optimization and Applications , vol. 56, no. 3, pp. 507–530, 2013
2013
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014, pp. 2672–2680
2014
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J. Zhang and B. Ghanem, “ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing,” in The IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1828–1837
2018
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W. Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu, “Denoising prior driven deep neural network for image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 41, no. 10, pp. 2305–2318, 2018
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2018
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2018
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C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
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A. Mousavi, A. B. Patel, and R. G. Baraniuk, “A deep learning approach to structured signal recovery,” in The 53rd Annual Allerton Conference on Communication, Control, and Computing . IEEE, 2015, pp. 1336–1343
2015
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D. P. Kingma and J. Ba, “ADAM: A method for stochastic optimization,” in International Conference on Learning Representations , 2015
2015
Cited alongside, same era.
C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,” IEEE Transactions on Information Theory , vol. 62, no. 9, pp. 5117–5144, 2016
2016
Cited alongside, same era.
J. Sun, H. Li, Z. Xu et al. , “Deep ADMM-Net for compressive sensing MRI,” in Advances in Neural Information Processing Systems , 2016, pp. 10–18
2016
Cited alongside, same era.
Z. Wang, Q. Ling, and T. S. Huang, “Learning deep l0 encoders,” in The Thirtieth AAAI Conference on Artificial Intelligence , 2016, pp. 2194–2200
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in The IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
Y. Liu, S. Wu, X. Huang, B. Chen, and C. Zhu, “Hybrid CS-DMRI: Periodic time-variant subsampling and omnidirectional total variation based reconstruction,” IEEE Transactions on Medical Imaging , vol. 36, no. 10, pp. 2148–2159, 2017
2017
Cited alongside, same era.
W. Cui, F. Jiang, X. Gao, W. Tao, and D. Zhao, “Deep neural network based sparse measurement matrix for image compressed sensing,” in 25th IEEE International Conference on Image Processing (ICIP) , 2018
2018
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J. Ma, X.-Y. Liu, Z. Shou, and X. Yuan, “Deep tensor ADMM-Net for snapshot compressive imaging,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 10 223–10 232
2019
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Z. Long, Y. Liu, L. Chen, and C. Zhu, “Low rank tensor completion for multiway visual data,” Signal Processing , vol. 155, pp. 301–316, 2019
2019
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Y. Liu, Z. Long, and C. Zhu, “Image completion using low tensor tree rank and total variation minimization,” IEEE Transactions on Multimedia , vol. 21, no. 2, pp. 338–350, 2019
2019
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Y. Wu, M. Rosca, and T. Lillicrap, “Deep compressed sensing,” in The Thirty-sixth International Conference on Machine Learning (ICML) , 2019, pp. 6850–6860
2019
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S. Ravishankar, J. C. Ye, and J. A. Fessler, “Image reconstruction: From sparsity to data-adaptive methods and machine learning,” Proceedings of IEEE , vol. 108, no. 1, pp. 86–109, 2019
2019
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W. Shi, F. Jiang, S. Liu, and D. Zhao, “Image compressed sensing using convolutional neural network,” IEEE Transactions on Image Processing , vol. 29, pp. 375–388, 2019
2019
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W. Shi, F. Jiang, S. Liu, and D. Zhao, “Scalable convolutional neural network for image compressed sensing,” in The IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 290–12 299
2019
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D. Gilton, G. Ongie, and R. Willett, “Neumann networks for linear inverse problems in imaging,” IEEE Transactions on Computational Imaging , vol. 6, pp. 328–343, 2019
2019
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J. Liu, C. Zhu, and Y. Liu, “Smooth compact tensor ring regression,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Closest in time.
Y. Liu, Z. Long, H. Huang, and C. Zhu, “Low CP rank and Tucker rank tensor completion for estimating missing components in image data,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 4, pp. 944–954, 2020
2020
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Y. Liu, J. Liu, and C. Zhu, “Low-rank tensor train coefficient array estimation for tensor-on-tensor regression,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 12, pp. 5402–5411, 2020
2020
Closest in time.
J. Liu, C. Zhu, Z. Long, H. Huang, and Y. Liu, “Low-rank tensor ring learning for multi-linear regression,” Pattern Recognition , 2020
2020
Closest in time.
M. Iliadis, L. Spinoulas, and A. K. Katsaggelos, “Deepbinarymask: Learning a binary mask for video compressive sensing,” Digital Signal Processing , vol. 96, p. 102591, 2020
2020
Closest in time.