| Deep learning for photoacoustic tomography from sparse data S Antholzer, M Haltmeier, J Schwab Inverse problems in science and engineering 27 (7), 987-1005, 2019 | 285 | 2019 |
| NETT: Solving inverse problems with deep neural networks H Li, J Schwab, S Antholzer, M Haltmeier Inverse Problems 36 (6), 065005, 2020 | 275 | 2020 |
| Deep null space learning for inverse problems: convergence analysis and rates J Schwab, S Antholzer, M Haltmeier Inverse Problems 35 (2), 025008, 2019 | 115 | 2019 |
| Photoacoustic image reconstruction via deep learning S Antholzer, M Haltmeier, R Nuster, J Schwab Photons plus ultrasound: Imaging and sensing 2018 10494, 433-442, 2018 | 65* | 2018 |
| Real-time photoacoustic projection imaging using deep learning J Schwab, S Antholzer, R Nuster, M Haltmeier arXiv preprint arXiv:1801.06693, 2018 | 54* | 2018 |
| NETT regularization for compressed sensing photoacoustic tomography S Antholzer, J Schwab, J Bauer-Marschallinger, P Burgholzer, ... Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 272-282, 2019 | 42 | 2019 |
| Learned backprojection for sparse and limited view photoacoustic tomography J Schwab, S Antholzer, M Haltmeier Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 263-271, 2019 | 26 | 2019 |
| Deep learning of truncated singular values for limited view photoacoustic tomography J Schwab, S Antholzer, R Nuster, G Paltauf, M Haltmeier Photons Plus Ultrasound: Imaging and Sensing 2019 10878, 254-262, 2019 | 26 | 2019 |
| Deep Learning Versus -Minimization for Compressed Sensing Photoacoustic Tomography S Antholzer, J Schwab, M Haltmeier 2018 IEEE International Ultrasonics Symposium (IUS), 206-212, 2018 | 23 | 2018 |
| Big in Japan: Regularizing networks for solving inverse problems J Schwab, S Antholzer, M Haltmeier Journal of mathematical imaging and vision 62 (3), 445-455, 2020 | 22 | 2020 |
| Deep synthesis network for regularizing inverse problems D Obmann, J Schwab, M Haltmeier Inverse Problems 37 (1), 015005, 2020 | 21* | 2020 |
| Regularization of inverse problems by filtered diagonal frame decomposition A Ebner, J Frikel, D Lorenz, J Schwab, M Haltmeier Applied and Computational Harmonic Analysis 62, 66-83, 2023 | 19 | 2023 |
| Augmented NETT regularization of inverse problems D Obmann, L Nguyen, J Schwab, M Haltmeier Journal of Physics Communications 5 (10), 105002, 2021 | 17 | 2021 |
| A Galerkin least squares approach for photoacoustic tomography J Schwab, S Pereverzyev Jr, M Haltmeier SIAM Journal on Numerical Analysis 56 (1), 160-184, 2018 | 17 | 2018 |
| Cryo-EM structure of the complete inner kinetochore of the budding yeast point centromere T Dendooven, Z Zhang, J Yang, SH McLaughlin, J Schwab, SHW Scheres, ... Science Advances 9 (30), eadg7480, 2023 | 13 | 2023 |
| Sparse anett for solving inverse problems with deep learning D Obmann, L Nguyen, J Schwab, M Haltmeier 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI …, 2020 | 11* | 2020 |
| Sparse synthesis regularization with deep neural networks D Obmann, J Schwab, M Haltmeier 2019 13th International conference on Sampling Theory and Applications …, 2019 | 7 | 2019 |
| DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images J Schwab, D Kimanius, A Burt, T Dendooven, S Scheres bioRxiv, 2023.10. 18.562877, 2023 | 6 | 2023 |
| Data-consistent neural networks for solving nonlinear inverse problems YE Boink, M Haltmeier, S Holman, J Schwab Inverse Problems and Imaging 17 (1), 203-229, 2023 | 5 | 2023 |
| Deep Learning for Image Reconstruction M Haltmeier, S Antholzer World Scientific Publishing, 2023 | 2 | 2023 |
Markus HaltmeierProfessor of Mathematics, University of Innsbruck在 uibk.ac.at 的电子邮件经过验证
