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E. J. Candès and Wakin, M. B. , An introduction to compressive sampling, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
G. Chen and Needell, D. , Compressed sensing and dictionary learning, Preprint, vol. 106, 2015.
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M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , Introduction to compressed sensing, preprint, vol. 93, p. 2, 2011.
D. L. Donoho and Tsaig, Y. , Fast solution of $$\backslash$ell \_ $\$1$\$ $-norm minimization problems when the solution may be sparse, IEEE Transactions on Information Theory, vol. 54, pp. 4789–4812, 2008.
J. Martin Duarte-Carvajalino and Sapiro, G. , Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization, IEEE Transactions on Image Processing, vol. 18, pp. 1395–1408, 2009.
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M. A. T. Figueiredo, Nowak, R. D. , and Wright, S. J. , Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of selected topics in signal processing, vol. 1, pp. 586–597, 2007.
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C. Hegde, Sankaranarayanan, A. C. , Yin, W. , and Baraniuk, R. G. , NuMax: A convex approach for learning near-isometric linear embeddings, IEEE Transactions on Signal Processing, vol. 63, pp. 6109–6121, 2015.
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K. Kulkarni, Lohit, S. , Turaga, P. , Kerviche, R. , and Ashok, A. , Reconnet: Non-iterative reconstruction of images from compressively sensed measurements, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 449–458, 2016.
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A. Mousavi, Patel, A. B. , and Baraniuk, R. G. , A deep learning approach to structured signal recovery, Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on. IEEE, pp. 1336–1343, 2015.
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S. Nam, Davies, M. E. , Elad, M. , and Gribonval, R. , The cosparse analysis model and algorithms, Applied and Computational Harmonic Analysis, vol. 34, pp. 30–56, 2013.
D. Needell and Tropp, J. A. , Cosamp: iterative signal recovery from incomplete and inaccurate samples, Communications of the ACM, vol. 53, pp. 93–100, 2010.
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H. Palangi, Ward, R. K. , and Deng, L. , Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors., ICASSP. pp. 3337–3341, 2013.
H. Palangi, Ward, R. K. , and Deng, L. , Distributed Compressive Sensing: A Deep Learning Approach., IEEE Trans. Signal Processing, vol. 64, pp. 4504–4518, 2016.
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S. Qaisar, Bilal, R. Muhammad, Iqbal, W. , Naureen, M. , and Lee, S. , Compressive sensing: From theory to applications, a survey, Journal of Communications and networks, vol. 15, pp. 443–456, 2013.
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Y. Shen, Zhu, G. , Li, J. , and Zhu, Z. , Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning, Proceedings of International Conference on Internet Multimedia Computing and Service. ACM, p. 193, 2014.
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J. A. Tropp and Gilbert, A. C. , Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on information theory, vol. 53, pp. 4655–4666, 2007.
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Y. Wang, Li, X. , Yu, H. , Ni, L. , Yang, W. , Weng, C. , and Zhao, J. , Optimizing boolean embedding matrix for compressive sensing in rram crossbar, Low Power Electronics and Design (ISLPED), 2015 IEEE/ACM International Symposium on. IEEE, pp. 13–18, 2015.