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The solution of inverse problems is crucial in various fields such as medicine, biology, and engineering, where one seeks to find a solution from noisy observations.
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Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography
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F. Natterer · 2012
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Analysis of iterative methods in photoacoustic tomography with variable sound speed
M. Haltmeier and L. V. Nguyen · 2017
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Modern regularization methods for inverse problems
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S. Lunz, O. Öktem, and C.-B. Schönlieb · 2018
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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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A uniform reconstruction formula in integral geometry
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Regularization methods in Banach spaces
T. Schuster, B. Kaltenbacher, B. Hofmann, and K. S. Kazimierski · 2012
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A primal–dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms
L. Condat · 2013
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Full-wave iterative image reconstruction in photoacoustic tomography with acoustically inhomogeneous media
C. Huang, K. Wang, L. Nie, L. V. Wang, and M. A. Anastasio · 2013
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Theory of linear ill-posed problems and its applications
V. K. Ivanov, V. V. Vasin, and V. P. Tanana · 2013
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Acoustic inversion in optoacoustic tomography: A review
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Deep null space learning for inverse problems: convergence analysis and rates
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A data-driven iteratively regularized Landweber iteration
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Data driven regularization by projection
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Regularization by architecture: A deep prior approach for inverse problems
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Total deep variation for linear inverse problems
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NETT: Solving inverse problems with deep neural networks
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Regularization of inverse problems by neural networks
M. Haltmeier and L. Nguyen · 2021
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End-to-end reconstruction meets data-driven regularization for inverse problems
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Augmented NETT regularization of inverse problems
D. Obmann, L. Nguyen, J. Schwab, and M. Haltmeier · 2021
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Regularization of inverse problems: Deep equilibrium models versus bilevel learning
D. Riccio, M. J. Ehrhardt, and M. Benning · 2022
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Data-proximal null-space networks for inverse problems
S. Göppel, J. Frikel, and M. Haltmeier · 2023
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A neural-network-based convex regularizer for inverse problems
A. Goujon, S. Neumayer, P. Bohra, S. Ducotterd, and M. Unser · 2023
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