Dr. Johannes Leuschner

Ehemaliger Mitarbeiter der WG Industrial Mathematics, Research Training Group π³


Information: Email ends with @uni-bremen.de

Research Areas

• Computed Tomography
• Deep Learning
• Inverse Problems

Courses (Selection)complete list

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

Theses (Selection)complete list

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

Publications (Selection)complete list

1. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
   Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
   Applied Mathematics for Modern Challenges, 1(2), 2023.

   DOI: 10.3934/ammc.2023007

2. J. Leuschner.
   Deep Learning for Computed Tomography Reconstruction: Learned Methods, Deep Image Prior, and Uncertaninty Estimation.
   Dissertationsschrift, Universität Bremen, 2023.

   DOI: 10.26092/elib/2704

3. J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
   Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
   Transactions on Machine Learning Research, 12, 2023.

   online at: https://openreview.net/forum?id=FWyabz82fH

4. T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
   Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
   Zur Veröffentlichung eingereicht.

   online at: https://aps.arxiv.org/abs/2307.10474

5. R. Barbano, J. Antorán, J. Leuschner, J. M. Hernández-Lobato, B. Jin, Z. Kereta.
   Image Reconstruction via Deep Image Prior Subspaces.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2302.10279

© Zentrum für Industriemathematik 2008-2023