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In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems.
1909
Earlier work this paper cites.
1912
Earlier work this paper cites.
R. Glowinski and A. Marroco, “Sur l’approximation, par éléments finis d’ordre un, et la résolution, par pénalisation-dualité d’une classe de problèmes de Dirichlet non linéaires,” RAIRO Anal. Numer. , vol. 9, no. R2, pp. 41–76, 1975
1975
Earlier work this paper cites.
D. Gabay and B. Mercier, “A dual algorithm for the solution of nonlinear variational problems via finite element approximation,” Comput. Math. Appl. , vol. 2, no. 1, pp. 17–40, 1976
1976
Earlier work this paper cites.
A. Tarantola and B. Valetta, “Inverse problems = quest for information,” J. Geophys. , vol. 50, no. 1, pp. 159–170, 1981
1981
Earlier work this paper cites.
J. Sietsma and R. J. Dow, “Creating artificial neural networks that generalize,” Neural Netw. , vol. 4, no. 1, pp. 67–79, 1991
1991
Earlier work this paper cites.
L. Holmstrom and P. Koistinen, “Using additive noise in back-propagation training,” IEEE Trans. Neural Netw. , vol. 3, no. 1, pp. 24–38, 1992
1992
Earlier work this paper cites.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena , vol. 60, no. 1–4, pp. 259–268, 1992
1992
Earlier work this paper cites.
E. T. Quinto, “Singularities of the X-Ray Transform and Limited Data Tomography in ℝ 2 \mathbb{R}^{2} and ℝ 3 \mathbb{R}^{3} ,” SIAM J. Math. Anal. , vol. 24, no. 5, pp. 1215–1225, 1993
1993
Earlier work this paper cites.
C. M. Bishop, “Training with noise is equivalent to Tikhonov regularization,” Neural Comput. , vol. 7, no. 1, pp. 108–116, 1995
1995
Earlier work this paper cites.
A. Chambolle and P.-L. Lions, “Image recovery via total variation minimization and related problems,” Numer. Math. , vol. 76, no. 2, pp. 167–188, 1997
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
J. L. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: A review,” Publ. Astron. Soc. Pac. , vol. 114, no. 800, pp. 1051–1069, 2002
2002
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
E. Kobler, A. Effland, K. Kunisch, and T. Pock, “Total deep variation: A stable regularizer for inverse problems,” 2020, arXiv: 2006.08789
2006
Earlier work this paper cites.
E. J. Candès, J. K. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory , vol. 52, no. 2, pp. 489–509, 2006
2006
Earlier work this paper cites.
J. Kaipio and E. Somersalo, Statistical and computational inverse problems , ser. Applied Mathematical Sciences. Springer New York, 2006, vol. 160
2006
Earlier work this paper cites.
M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. , vol. 25, no. 2, pp. 72–82, 2008
2008
Earlier work this paper cites.
J. Haupt, W. U. Bajwa, M. Rabbat, and R. Nowak, “Compressed sensing for networked data,” IEEE Signal Process. Mag. , vol. 25, no. 2, pp. 92–101, 2008
2008
Earlier work this paper cites.
E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. , vol. 53, no. 17, pp. 4777–4807, 2008
2008
Earlier work this paper cites.
2009
Earlier work this paper cites.
K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML) , J. Fürnkranz and T. Joachims, Eds., 2010, pp. 399–406
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J. Mach. Learn. Res. , vol. 11, pp. 3371–3408, 2010
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105
2012
Earlier work this paper cites.
J. L. Mueller and S. Siltanen, Linear and nonlinear inverse problems with practical applications . SIAM, 2012
2012
Earlier work this paper cites.
S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing , ser. Applied and Numerical Harmonic Analysis. Birkhäuser Basel, 2013
2013
Cited alongside, same era.
D. Needell and R. Ward, “Near-optimal compressed sensing guarantees for total variation minimization,” IEEE Trans. Imag. Proc. , vol. 22, no. 10, pp. 3941–3949, 2013
2013
Cited alongside, same era.
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” 2014, preprint arXiv:1412.6980
2014
Cited alongside, same era.
D. Amelunxen, M. Lotz, M. B. McCoy, and J. A. Tropp, “Living on the edge: phase transitions in convex programs with random data,” Inf. Inference , vol. 3, no. 3, pp. 224–294, 2014
J. Adler and O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Probl. , vol. 33, no. 12, p. 124007, 2017
2017
Later among the works it cites.
H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Trans. Comput. Imag. , vol. 3, no. 1, pp. 47–57, 2017
2017
Later among the works it cites.
A. S. Bandeira, D. G. Mixon, and B. Recht, “Compressive classification and the rare eclipse problem,” in Compressed Sensing and its Applications: Second International MATHEON Conference 2015 , ser. Applied and Numerical Harmonic Analysis, H. Boche, G. Caire, R. Calderbank, M. März, G. Kutyniok, and R. Mathar, Eds. Springer Cham, 2017, pp. 197–220
2017
Later among the works it cites.
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, 2018
2018
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2014
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Cited alongside, same era.
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. Springer Cham, 2015, pp. 234–241
2015
Cited alongside, same era.
C. Poon, “On the role of total variation in compressed sensing,” SIAM J. Imag. Sci. , vol. 8, no. 1, pp. 682–720, 2015
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016
2016
Cited alongside, same era.
Y. Yang, J. Sun, H. Li, and Z. Xu, “Deep ADMM-Net for compressive sensing MRI,” in Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 10–18
2016
Cited alongside, same era.
C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 2, pp. 295–307, 2016
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
Later among the works it cites.
B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature , vol. 555, no. 7697, pp. 487–492, 2018
2018
Later among the works it cites.
Y. Huang, T. Würfl, K. Breininger, L. Liu, G. Lauritsch, and A. Maier, “Some investigations on robustness of deep learning in limited angle tomography,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 , A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, and G. Fichtinger, Eds. Springer Cham, 2018, pp. 145–153
2018
Later among the works it cites.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1625–1634
2018
Later among the works it cites.
2018
Later among the works it cites.
Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1348–1357, 2018
2018
Later among the works it cites.
H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: model-based deep learning architecture for inverse problems,” IEEE Trans. Med. Imag. , vol. 38, no. 2, pp. 394–405, 2018
2018
Later among the works it cites.
J. Adler and O. Öktem, “Learned primal-dual reconstruction,” IEEE Trans. Med. Imag. , vol. 37, no. 6, pp. 1322–1332, 2018
2018
Later among the works it cites.
M. Benning and M. Burger, “Modern regularization methods for inverse problems,” Acta Numer. , vol. 27, pp. 1–111, 2018
2018
Later among the works it cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9446–9454
2018
Later among the works it cites.
T. A. Bubba, G. Kutyniok, M. Lassas, M. März, W. Samek, S. Siltanen, and V. Srinivasan, “Learning the invisible: A hybrid deep learning-shearlet framework for limited angle computed tomography,” Inverse Probl. , vol. 35, no. 6, p. 064002, 2019
2019
Later among the works it cites.
S. Arridge, P. Maass, O. Öktem, and C.-B. Schönlieb, “Solving inverse problems using data-driven models,” Acta Numer. , vol. 28, pp. 1–174, 2019
2019
Later among the works it cites.
X. Yuan, P. He, Q. Zhu, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 30, no. 9, pp. 2805–2824, 2019
2019
Later among the works it cites.
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter, “Differentiable convex optimization layers,” in Advances in Neural Information Processing Systems 32 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 9562–9574
2019
Later among the works it cites.
A. Hauptmann, J. Adler, S. R. Arridge, and O. Öktem, “Multi-scale learned iterative reconstruction,” IEEE Trans. Comput. Imag. , 2020, available online: https://doi.org/10.1109/TCI.2020.2990299
2020
Closest in time.
V. Antun, F. Renna, C. Poon, B. Adcock, and A. C. Hansen, “On instabilities of deep learning in image reconstruction and the potential costs of AI,” Proc. Natl. Acad. Sci. , 2020, available online: https://doi.org/10.1073/pnas.1907377117
2020
Closest in time.
F. Knoll, J. Zbontar, A. Sriram, M. J. Muckley, M. Bruno, A. Defazio, M. Parente, K. J. Geras, J. Katsnelson, H. Chandarana, Z. Zhang, M. Drozdzalv, A. Romero, M. Rabbat, P. Vincent, J. Pinkerton, D. Wang, N. Yakubova, E. Owens, C. L. Zitnick, M. P. Recht, D. K. Sodickson, and Y. W. Lui, “fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,” Radiology Artif. Intell. , vol. 2, no. 1, p. e190007, 2020
2020
Closest in time.
N. Carlini, “A Complete List of All (arXiv) Adversarial Example Papers,” available online: https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html , accessed on 2020-11-02, 2020
2020
Closest in time.
A. Arnab, O. Miksik, and P. H. Torr, “On the robustness of semantic segmentation models to adversarial attacks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 12, pp. 3040–3053, 2020
2020
Closest in time.
G. Ongie, A. Jalal, R. G. Baraniuk, C. A. Metzler, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE J. Sel. Areas Inf. Theory , vol. 1, no. 1, pp. 39–56, 2020
2020
Closest in time.
I. Y. Chun, Z. Huang, H. Lim, and J. Fessler, “Momentum-Net: fast and convergent iterative neural network for inverse problems,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020, available online: https://doi.org/10.1109/TPAMI.2020.3012955
2020
Closest in time.
P. Ernst, “Pytorch implementation of scikit-image’s radon function, version 0.1.4,” available online: https://github.com/phernst/pytorch_radon , 2020
2020
Closest in time.
H. Li, J. Schwab, S. Antholzer, and M. Haltmeier, “NETT: solving inverse problems with deep neural networks,” Inverse Probl. , vol. 36, no. 6, p. 065005, 2020
2020
Closest in time.
F. Knoll, T. Murrell, A. Sriram, N. Yakubova, J. Zbontar, M. Rabbat, A. Defazio, M. J. Muckley, D. K. Sodickson, C. L. Zitnick, and M. P. Recht, “Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge,” Magn. Reson. Med. , vol. 84, no. 6, pp. 3054–3070, 2020
2020
Closest in time.
K. Cheng, F. Calivá, R. Shah, M. Han, S. Majumdar, and V. Pedoia, “Addressing the false negative problem of deep learning MRI reconstruction models by adversarial attacks and robust training,” in Proceedings of the 3rd Conference on Medical Imaging with Deep Learning (MIDL) , T. Arbel, I. B. Ayed, M. de Bruijne, M. Descoteaux, H. Lombaert, and C. Pal, Eds., vol. 121, 2020, pp. 121–135
2020
Closest in time.