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. 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

2. 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

3. 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

4. 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

5. M. Nittscher, M. F. Lameter, R. Barbano, J. Leuschner, B. Jin, P. Maaß.
   SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.
   Medical Imaging with Deep Learning (MIDL 2023), 10.07.-12.07.2023.
   

   online at: https://2023.midl.io/papers/p014

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