| Computed tomography reconstruction using deep image prior and learned reconstruction methods DO Baguer, J Leuschner, M Schmidt Inverse Problems 36 (9), 094004, 2020 | 160 | 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 | 114* | 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 | 53 | 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 | 44 | 2021 |
| Conditional invertible neural networks for medical imaging A Denker, M Schmidt, J Leuschner, P Maass Journal of Imaging 7 (11), 243, 2021 | 36 | 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 | 19* | 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 | 17 | 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 | 10* | 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 | 7 | 2022 |
| Deep inversion validation library J Leuschner, M Schmidt, D Erzmann Software available from https://github. com/jleuschn/dival, 2019 | 7 | 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 | 3 | 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 | 3 | 2021 |
| Fast and Painless Image Reconstruction in Deep Image Prior Subspaces R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, ® Kereta, ... arXiv preprint arXiv 2302, 2023 | 2 | 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 | | 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 | | 2023 |
| Deep learning for computed tomography reconstruction-learned methods, deep image prior and uncertainty estimation J Leuschner Universität Bremen, 2023 | | 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 | | 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 | | 2023 |
| The Deep Capsule Prior–advantages through complexity? M Schmidt, A Denker, J Leuschner PAMM 21 (1), e202100166, 2021 | | 2021 |
| A Benchmark for Deep Learning Reconstruction Methods for Low-Dose Computed Tomography M Schmidt, J Leuschner, DO Baguer, P Maaß | | 2020 |
