Johannes Leuschner

Forschungsgebiete

• Computertomographie
• Deep Learning
• Inverse Probleme

Veranstaltungen (Auswahl)vollständige Liste

1. Computerpraktikum (Wintersemester 2020/2021)
2. Computerpraktikum (Wintersemester 2019/2020)

Abschlussarbeiten (Auswahl)vollständige Liste

1. Modellierung von Geometrieabweichungen bei der Nano-Computertomographie (Tom Lütjen)
2. Using Neural Networks to Denoise CT Images (Rudolf Herdt)

Publikationen (Auswahl)vollständige Liste

1. 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

2. 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

3. 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.

   DOI: 10.3390/jimaging7110243

4. 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.

   DOI: 10.1002/pamm.202100166

5. 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.

   DOI: 10.1038/s41597-021-00893-z