| Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography TA Bubba, G Kutyniok, M Lassas, M März, W Samek, S Siltanen, ... Inverse Problems 35 (6), 064002 - Authors Alphabetically Sorted, 2019 | 155 | 2019 |
| Solving inverse problems with deep neural networks - robustness included? M Genzel, J Macdonald, M März IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 139 | 2022 |
| ℓ1-analysis minimization and generalized (co-) sparsity: when does recovery succeed? M Genzel, G Kutyniok, M März Applied and Computational Harmonic Analysis 52, 82-140, 2021 | 42 | 2021 |
| Near-exact recovery for tomographic inverse problems via deep learning M Genzel, I Gühring, J Macdonald, M März International Conference on Machine Learning, 7368-7381, 2022 | 33 | 2022 |
| Tomographic X-ray data of carved cheese TA Bubba, M Juvonen, J Lehtonen, M März, A Meaney, Z Purisha, ... arXiv preprint arXiv:1705.05732, 2017 | 29 | 2017 |
| Interval neural networks: Uncertainty scores L Oala, C Heiß, J Macdonald, M März, W Samek, G Kutyniok arXiv preprint arXiv:2003.11566, 2020 | 27 | 2020 |
| Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting J Ma, M März, S Funk, J Schulz-Menger, G Kutyniok, T Schaeffter, ... Physics in Medicine & Biology 63 (23), 235004, 2018 | 26 | 2018 |
| Shearlet-based regularization in sparse dynamic tomography TA Bubba, M März, Z Purisha, M Lassas, S Siltanen Wavelets and Sparsity XVII 10394, 236-245, 2017 | 20 | 2017 |
| A multilevel based reweighting algorithm with joint regularizers for sparse recovery J Ma, M März arXiv preprint arXiv:1604.06941, 2016 | 18 | 2016 |
| Sampling Rates for -Synthesis M März, C Boyer, J Kahn, P Weiss Foundations of Computational Mathematics 23 (6), 2089-2150, 2023 | 10 | 2023 |
| Detecting failure modes in image reconstructions with interval neural network uncertainty L Oala, C Heiß, J Macdonald, M März, G Kutyniok, W Samek International Journal of Computer Assisted Radiology and Surgery 16, 2089-2097, 2021 | 10 | 2021 |
| Correcting the side effects of ADC filtering in MR image reconstruction C Lazarus, M März, P Weiss Journal of Mathematical Imaging and Vision 62, 1034-1047, 2020 | 10 | 2020 |
| Compressed sensing with 1D total variation: Breaking sample complexity barriers via non-uniform recovery M Genzel, M März, R Seidel Information and Inference: A Journal of the IMA 11 (1), 203-250, 2022 | 9 | 2022 |
| Compressed Sensing and Its Applications: Second International MATHEON Conference 2015 H Boche, G Caire, R Calderbank, M März, G Kutyniok, R Mathar Birkhäuser, 2018 | 7 | 2018 |
| Mathematical methods in medical image processing G Kutyniok, J Ma, M März Quantification of Biophysical Parameters in Medical Imaging, 153-166, 2018 | 7 | 2018 |
| AAPM DL-sparse-view CT challenge submission report: Designing an iterative network for fanbeam-CT with unknown geometry M Genzel, J Macdonald, M März arXiv preprint arXiv:2106.00280, 2021 | 6 | 2021 |
| Solving inverse problems with deep neural networks–robustness included?. arXiv preprint.(2020) M Genzel, J Macdonald, M März arXiv preprint arXiv:2011.04268, 0 | 6 | |
| Image quality improvement using short range finite difference in QSM reconstruction M Maerz, D Zhou, Y Zhang, P Spincemaille, L Ruthotto, Y Wang Proc. 23rd Annu. Meeting Exhib. Int. Soc. Magn. Reson. Med., 2015 | 3 | 2015 |
| Interval neural networks as instability detectors for image reconstructions J Macdonald, M März, L Oala, W Samek Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on …, 2021 | 2 | 2021 |
| Multilevel Gauß-Newton Methoden zur Phasenrekonstruktion R Beinert | 2 | 2013 |
