Johannes Leuschner
Wissenschaftlicher Mitarbeiter der AG Technomathematik, Research Training Group π3Raum: MZH 2050
E-Mail: jleuschn@uni-bremen.de
Telefon: (0421) 218-63811
ORCID iD: 0000-0001-7361-9523
E-Mail: jleuschn@uni-bremen.de
Telefon: (0421) 218-63811
ORCID iD: 0000-0001-7361-9523
Forschungsgebiete
- Computertomographie
- Deep Learning
- Inverse Probleme
Veranstaltungen (Auswahl)
- Computerpraktikum (Wintersemester 2020/2021)
- Computerpraktikum (Wintersemester 2019/2020)
Abschlussarbeiten (Auswahl)
- Modellierung von Geometrieabweichungen bei der Nano-Computertomographie (Tom Lütjen)
- Using Neural Networks to Denoise CT Images (Rudolf Herdt)
Publikationen (Auswahl)
- J. Leuschner, M. Schmidt, P. Ganguly, V. Andriiashen, S. Coban, A. Denker, D. Bauer, A. Hadjifaradji, K. Batenburg, B. Maass, M. von Eijnatten.
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
MDPI Journal of Imaging, 7(3), 44 S., 2021.DOI: 10.3390/jimaging7030044
online unter: https://www.mdpi.com/2313-433X/7/3/44 - S. Schulze, J. Leuschner, E. King.
Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients.
MDPI Open Access Journals Signals, 2(4):637-661, 2021.DOI: 10.3390/signals2040039
online unter: https://www.mdpi.com/2624-6120/2/4/39 - A. Denker, M. Schmidt, J. Leuschner, P. Maaß.
Conditional Invertible Neural Networks for Medical Imaging .
MDPI Journal of Imaging, Inverse Problems and Imaging 7(11), 243 S., 2021. - M. Schmidt, A. Denker, J. Leuschner.
The Deep Capsule Prior - advantages through complexity.
GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021. - J. Leuschner, M. Schmidt, D. Otero Baguer, P. Maaß.
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
Scientific Data, 8(109), 2021.