NETT: Solving inverse problems with deep neural networks H Li, J Schwab, S Antholzer, M Haltmeier
Inverse Problems 36 (6), 065005, 2020
321 2020 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
317 2019 Deep null space learning for inverse problems: convergence analysis and rates J Schwab, S Antholzer, M Haltmeier
Inverse Problems 35 (2), 025008, 2019
133 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
68 2018 A joint deep learning approach for automated liver and tumor segmentation N Gruber, S Antholzer, W Jaschke, C Kremser, M Haltmeier
2019 13th international conference on sampling theory and applications …, 2019
66 2019 Real-time photoacoustic projection imaging using deep learning J Schwab, S Antholzer, R Nuster, M Haltmeier
arXiv preprint arXiv:1801.06693, 2018
55 * 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
46 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
27 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 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
23 2020 Deep Learning Versus S Antholzer, J Schwab, M Haltmeier
2018 IEEE International Ultrasonics Symposium (IUS), 206-212, 2018
23 2018 Discretization of learned NETT regularization for solving inverse problems S Antholzer, M Haltmeier
Journal of Imaging 7 (11), 239, 2021
11 2021 Hybrid immediacy: designing with artificial neural networks through physical concept modelling M Bank, V Sandor, R Kraft, S Antholzer, M Berger, T Fabini, B Kovacs, ...
Design Modelling Symposium Berlin, 13-23, 2022
6 2022 Photons Plus Ultrasound: Imaging and Sensing 2018 S Antholzer, M Haltmeier, R Nuster, J Schwab
San Francisco, USA U 104944, 2018
6 2018 Compressive time-of-flight 3D imaging using block-structured sensing matrices S Antholzer, C Wolf, M Sandbichler, M Dielacher, M Haltmeier
Inverse Problems 35 (4), 045004, 2019
4 * 2019 Compressive time-of-flight imaging S Antholzer, C Wolf, M Sandbichler, M Dielacher, M Haltmeier
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
3 2017 Deep Learning for Image Reconstruction M Haltmeier, S Antholzer
World Scientific Publishing, 2023
2 2023 Cluster-Based Autoencoders for Volumetric Point Clouds S Antholzer, M Berger, T Hell
arXiv preprint arXiv:2211.01009, 2022
2022 Correction to: Hybrid Immediacy: Designing with Artificial Neural Networks Through Physical Concept Modelling M Bank, V Sandor, R Kraft, S Antholzer, M Berger, T Fabini, B Kovacs, ...
Design Modelling Symposium Berlin, C1-C1, 2022
2022 Sampling and resolution in sparse view photoacoustic tomography M Haltmeier, D Obmann, F Dreier, S Antholzer, K Felbermayer, ...
European Conference on Biomedical Optics, ES2C. 2, 2021
2021 
Markus HaltmeierProfessor of Mathematics, University of InnsbruckEmail verificata su uibk.ac.at