| Computed tomography reconstruction using deep image prior and learned reconstruction methods DO Baguer, J Leuschner, M Schmidt Inverse Problems 36 (9), 094004, 2020 | 60 | 2020 |
| The lodopab-ct dataset: A benchmark dataset for low-dose ct reconstruction methods J Leuschner, M Schmidt, DO Baguer, P Maaß arXiv preprint arXiv:1910.01113, 2019 | 32 | 2019 |
| 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 | 32 | 2019 |
| LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction J Leuschner, M Schmidt, DO Baguer, P Maass Scientific Data 8 (1), 1-12, 2021 | 15 | 2021 |
| 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 | 10 | 2021 |
| 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 | 8 | 2020 |
| Deep inversion validation library J Leuschner, M Schmidt, D Erzmann Software available from https://github. com/jleuschn/dival, 2019 | 5 | 2019 |
| Conditional Invertible Neural Networks for Medical Imaging A Denker, M Schmidt, J Leuschner, P Maass Journal of Imaging 7 (11), 243, 2021 | 2 | 2021 |
| Is Deep Image Prior in Need of a Good Education? R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maaß, ... arXiv preprint arXiv:2111.11926, 2021 | 1 | 2021 |
| 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 | 1 | 2021 |
| The LoDoPaB-CT Dataset J Leuschner, M Schmidt, DO Baguer, P Maaß arXiv preprint arXiv:1910.01113, 2019 | 1 | 2019 |
| A Probabilistic Deep Image Prior for Computational Tomography J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin arXiv preprint arXiv:2203.00479, 2022 | | 2022 |
| The Deep Capsule Prior–advantages through complexity? M Schmidt, A Denker, J Leuschner PAMM 21 (1), e202100166, 2021 | | 2021 |
| Training a Deep Neural Network via Policy Gradients for Blind Source Separation in Polyphonic Music Recordings S Schulze, J Leuschner, EJ King arXiv e-prints, arXiv: 2107.04235, 2021 | | 2021 |
| A Benchmark for Deep Learning Reconstruction Methods for Low-Dose Computed Tomography M Schmidt, J Leuschner, DO Baguer, P Maaß | | 2020 |
| Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers C Brandt, M Hamann, J Leuschner Universität Bremen, Zentrum für Technomathematik, Fachbereich 3-Mathematik …, 2019 | | 2019 |
| Zentrum für Technomathematik C Brandt, M Hamann, J Leuschner International Workshop on Magnetic Particle Imaging (IWMPI) Book of …, 2017 | | 2017 |
