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Inverse problems are inherently ill-posed and therefore require regularization techniques to achieve a stable solution.
Singularities of the X-ray transform and limited data tomography in ℝ 2 \mathbb{R}^{2} and ℝ 3 \mathbb{R}^{3}
E. T. Quinto · 1993
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F. Natterer · 2001
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O. Ronneberger, P. Fischer, and T. Brox · 2015
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J. Frikel and E. T. Quinto · 2016
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Solving ill-posed inverse problems using iterative deep neural networks
J. Adler and O. Öktem · 2017
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Deep convolutional neural network for inverse problems in imaging
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser · 2017
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A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction
E. Kang, J. Min, and J. Ye · 2017
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Deep residual learning for compressed sensing mri
D. Lee, J. Yoo, and J. C. Ye · 2017
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Artifacts and visible singularities in limited data x-ray tomography
E. T. Quinto · 2017
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Deep learning microscopy
Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan · 2017
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A deep cascade of convolutional neural networks for dynamic MR image reconstruction
J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price, and D. Rueckert · 2018
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Deep learning for photoacoustic tomography from sparse data
S. Antholzer, M. Haltmeier, and J. Schwab · 2019
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Solving inverse problems using data-driven models
S. Arridge, P. Maass, O. Öktem, and C.-B. Schönlieb · 2019
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Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
T. A. Bubba, G. Kutyniok, M. Lassas, M. März, W. Samek, S. Siltanen, and V. Srinivasan · 2019
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Deep null space learning for inverse problems: convergence analysis and rates
J. Schwab, S. Antholzer, and M. Haltmeier · 2019
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Deep decomposition learning for inverse imaging problems
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Amortised map inference for image super-resolution
C. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Huszár · 2017
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Reweighted anisotropic total variation minimization for limited-angle ct reconstruction
T. Wang, K. Nakamoto, H. Zhang, and H. Liu · 2017
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Modern regularization methods for inverse problems
M. Benning and M. Burger · 2018
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Analyzing reconstruction artifacts from arbitrary incomplete x-ray ct data
L. Borg, J. S. Jørgensen, J. Frikel, and E. T. Quinto · 2018
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Learning a variational network for reconstruction of accelerated MRI data
K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll · 2018
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A u-nets cascade for sparse view computed tomography
A. Kofler, M. Haltmeier, C. Kolbitsch, M. Kachelrieß, and M. Dewey · 2018
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D. Chen and M. E. Davies · 2020
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NETT: solving inverse problems with deep neural networks
H. Li, J. Schwab, S. Antholzer, and M. Haltmeier · 2020
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Deep learning techniques for inverse problems in imaging
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett · 2020
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Big in Japan: Regularizing networks for solving inverse problems
J. Schwab, S. Antholzer, and M. Haltmeier · 2020
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Deep learning for tomographic image reconstruction
G. Wang, J. C. Ye, and B. De Man · 2020
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Recurrent variational network: A deep learning inverse problem solver applied to the task of accelerated mri reconstruction, 2022
G. Yiasemis, J.-J. Sonke, C. Sánchez, and J. Teuwen · 2022
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PatchNR: learning from very few images by patch normalizing flow regularization
F. Altekrüger, A. Denker, P. Hagemann, J. Hertrich, P. Maass, and G. Steidl · 2023
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Solving inverse problems with deep neural networks – robustness included?
M. Genzel, J. Macdonald, and M. März · 2023
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