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Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints.
S. Chen, D. Donoho, and S. M. review, “Atomic decomposition by basis pursuit,” SIAM review , 2001
2001
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M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging.” Magn Reson Med , vol. 58, no. 6, pp. 1182–95, 2007
2007
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K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” in Proceedings of the 27th International Conference on International Conference on Machine Learning , ser. ICML’10. USA: Omnipress, 2010, pp. 399–406. [Online]. Available: http://dl.acm.org/citation.cfm?id=3104322.3104374
2010
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S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning , vol. 3, no. 1, pp. 1–122, 2011
2011
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M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “Espirit—an eigenvalue approach to autocalibrating parallel mri: Where sense meets grappa,” Magnetic Resonance in Medicine , vol. 71, no. 3, pp. 990–1001, 2014. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.24751
2014
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 234–241
2015
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2017
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J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for dynamic MR image reconstruction.” IEEE Trans Med Imaging , vol. 37, no. 2, pp. 491–503, 2017
2017
Cited alongside, same era.
K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll, “Learning a variational network for reconstruction of accelerated MRI data.” Magn Reson Med , vol. 79, no. 6, pp. 3055–3071, 2017
2017
Cited alongside, same era.
V. Papyan, Y. Romano, and E. M. of Research, “Convolutional neural networks analyzed via convolutional sparse coding,” The Journal of Machine Learning Research , 2017
2017
Cited alongside, same era.
H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: model based deep learning architecture for inverse problems,” Ieee T Med Imaging , vol. PP, no. 99, pp. 1–1, 2018
2018
Cited alongside, same era.
F. Ong, “Low dimensional methods for high dimensional magnetic resonance imaging,” 2018
2018
Later among the works it cites.
M. Uecker and J. I. Tamir, “bart: version 0.4.04,” 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
J. I. Tamir, S. X. Yu, and M. Lustig, “Unsupervised deep basis pursuit: Learning reconstruction without ground-truth data,” in Proc. Intl. Soc. Mag. Reson. Med 27 , vol. 27, Montreal, May 2019, p. 0660
2019
Closest in time.
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D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9446–9454
2018
Cited alongside, same era.
J. Lehtinen, J. Munkberg, J. Hasselgren, and L. S. preprint arXiv …, “Noise2noise: Learning image restoration without clean data,” arXiv preprint arXiv … , 2018
2018
Cited alongside, same era.
2019
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