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. 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. 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

3. D. Otero Baguer, J. Leuschner, M. Schmidt.
   Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
   Inverse Problems, 36(9), IOPscience, 2020.

   DOI: 10.1088/1361-6420/aba415

4. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
   Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
   ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.
   

   online unter: https://invertibleworkshop.github.io/accepted_papers/index.html

5. C. Brandt, M. Hamann, J. Leuschner.
   Regression Models for Ultrasonic Testing of Carbon Fiber Reinforced Polymers.
   Berichte aus der Technomathematik 19–01, Universität Bremen, 2019.