Johannes Leuschner
Johannes Leuschner
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Cited by
Cited by
Computed tomography reconstruction using deep image prior and learned reconstruction methods
DO Baguer, J Leuschner, M Schmidt
Inverse Problems 36 (9), 094004, 2020
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction
J Leuschner, M Schmidt, DO Baguer, P Maass
Scientific Data 8 (1), 109, 2021
Supervised non-negative matrix factorization methods for MALDI imaging applications
J Leuschner, M Schmidt, P Fernsel, D Lachmund, T Boskamp, P Maass
Bioinformatics 35 (11), 1940-1947, 2019
Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications
J Leuschner, M Schmidt, PS Ganguly, V Andriiashen, SB Coban, ...
Journal of Imaging 7 (3), 44, 2021
Conditional invertible neural networks for medical imaging
A Denker, M Schmidt, J Leuschner, P Maass
Journal of Imaging 7 (11), 243, 2021
An educated warm start for deep image prior-based micro CT reconstruction
R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maass, ...
IEEE Transactions on Computational Imaging 8, 1210-1222, 2022
Conditional normalizing flows for low-dose computed tomography image reconstruction
A Denker, M Schmidt, J Leuschner, P Maass, J Behrmann
arXiv preprint arXiv:2006.06270, 2020
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin
arXiv preprint arXiv:2203.00479, 2022
Bayesian experimental design for computed tomography with the linearised deep image prior
R Barbano, J Leuschner, J Antorán, B Jin, JM Hernández-Lobato
arXiv preprint arXiv:2207.05714, 2022
Deep inversion validation library
J Leuschner, M Schmidt, D Erzmann
Software available from https://github. com/jleuschn/dival, 2019
Svd-dip: Overcoming the overfitting problem in dip-based ct reconstruction
M Nittscher, MF Lameter, R Barbano, J Leuschner, B Jin, P Maass
Medical Imaging with Deep Learning, 617-642, 2024
Blind source separation in polyphonic music recordings using deep neural networks trained via policy gradients
S Schulze, J Leuschner, EJ King
Signals 2 (4), 637-661, 2021
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces
R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, Ž Kereta, ...
arXiv preprint arXiv 2302, 2023
Image Reconstruction via Deep Image Prior Subspaces
R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, B Jin, ...
arXiv preprint arXiv:2302.10279, 2023
Learning-based approaches for reconstructions with inexact operators in nanoCT applications
T Lütjen, F Schönfeld, A Oberacker, J Leuschner, M Schmidt, A Wald, ...
IEEE Transactions on Computational Imaging, 2024
Model-based deep learning approaches to the Helsinki Tomography Challenge 2022
C Arndt, A Denker, S Dittmer, J Leuschner, J Nickel, M Schmidt
Applied Mathematics for Modern Challenges 1 (2), 87-104, 2023
Deep learning for computed tomography reconstruction-learned methods, deep image prior and uncertainty estimation
J Leuschner
Universität Bremen, 2023
In Focus-hybrid deep learning approaches to the HDC2021 challenge.
C Arndt, A Denker, J Nickel, J Leuschner, M Schmidt, G Rigaud
Inverse Problems & Imaging 17 (5), 2023
The Deep Capsule Prior–advantages through complexity?
M Schmidt, A Denker, J Leuschner
PAMM 21 (1), e202100166, 2021
A Benchmark for Deep Learning Reconstruction Methods for Low-Dose Computed Tomography
M Schmidt, J Leuschner, DO Baguer, P Maaß
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