Articles with public access mandates - Johannes LeuschnerLearn more
Available somewhere: 11
Computed tomography reconstruction using deep image prior and learned reconstruction methods
DO Baguer, J Leuschner, M Schmidt
Inverse Problems 36 (9), 094004, 2020
Mandates: German Research Foundation
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
Mandates: German Research Foundation
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
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
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
Mandates: German Research Foundation, Netherlands Organisation for Scientific Research …
Conditional invertible neural networks for medical imaging
A Denker, M Schmidt, J Leuschner, P Maass
Journal of Imaging 7 (11), 243, 2021
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
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
Mandates: National Natural Science Foundation of China, German Research Foundation …
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
Mandates: German Research Foundation, UK Engineering and Physical Sciences Research …
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
Mandates: UK Engineering and Physical Sciences Research Council
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
Mandates: German Research Foundation
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.
R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, Z Kereta, ...
arXiv preprint arXiv:2302.10279, 2023
Mandates: German Research Foundation, UK Engineering and Physical Sciences Research …
The Deep Capsule Prior–advantages through complexity?
M Schmidt, A Denker, J Leuschner
PAMM 21 (1), e202100166, 2021
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
Publication and funding information is determined automatically by a computer program