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Solving ill-posed inverse problems using iterative deep neural networks
PaperIOP Institute of Physics | 2017| -
Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
PaperIOP Institute of Physics | 2019| -
Learning about physical parameters: the importance of model discrepancy
PaperIOP Institute of Physics | 2014| -
Compressed sensing recovery via nonconvex shrinkage penalties
PaperIOP Institute of Physics | 2016| -
Computed tomography reconstruction using deep image prior and learned reconstruction methods
Free accessPaperIOP Institute of Physics | 2020| -
Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction
PaperIOP Institute of Physics | 2018| -
An optimal transport approach for seismic tomography: application to 3D full waveform inversion
PaperIOP Institute of Physics | 2016| -
A tutorial on inverse problems for anomalous diffusion processes
Free accessPaperIOP Institute of Physics | 2015| -
Hybrid PET-MR list-mode kernelized expectation maximization reconstruction
Free accessPaperIOP Institute of Physics | 2019| -
A penalty method for PDE-constrained optimization in inverse problems
PaperIOP Institute of Physics | 2016| -
Deep null space learning for inverse problems: convergence analysis and rates
Free accessPaperIOP Institute of Physics | 2019| -
A method for quantitative imaging of electrical properties of human tissues from only amplitude electromagnetic data
PaperIOP Institute of Physics | 2019| -
Geometric properties of solutions to the total variation denoising problem
PaperIOP Institute of Physics | 2017| -
A graph space optimal transport distance as a generalization of L p distances: application to a seismic imaging inverse problem
PaperIOP Institute of Physics | 2019| -
Uniqueness and Lipschitz stability in electrical impedance tomography with finitely many electrodes
PaperIOP Institute of Physics | 2019|
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