Dr. Matthias Beckmann

Courses (Selection)complete list

1. Mathematical Methods in Machine Learning (Wintersemester 2024/2025)
2. Advanced Topics in Image Processing – The Beauty of Variational Calculus (Wintersemester 2024/2025)
3. Mathematical Foundations of Machine Learning (Sommersemester 2024)
4. Mathematical Foundations of Machine Learning (Sommersemester 2023)
5. Inverse Problems (Wintersemester 2022/2023)

Theses (Selection)complete list

1. Inversion of the Modulo Radon Transform via Laplacian Phase Unwrapping (Carla Dittert)
2. The Radon Cumulative Distribution Transform in Image Classification (Lia Pribnow)
3. Inversion of the Modulo Radon Transform via direct Fourier Reconstruction Methods (Meira Iske)
4. Equivariant Neural Networks for Indirect Measurements (Nick Heilenkötter)
5. Approximation nichtlinearer Operatoren durch Neuronale Netze und ihre Implementierung durch DeepONets (Theresa Sauerland)

Publications (Selection)complete list

1. M. Beckmann, J. Nickel.
   Optimized filter functions for filtered back projection reconstructions.
   Inverse Problems and Imaging, , 2025.

   DOI: 10.3934/ipi.2025003

2. D. Hauser, M. Beckmann, G. Koliander, H. S. Stiehl.
   On Image Processing and Pattern Recognition for Thermograms of Watermarks in Manuscripts - A First Proof-of-Concept.
   18th International Conference on Document Analysis and Recognition (ICDAR), 30.08.-04.09.2024.
   

   DOI: 10.1007/978-3-031-70543-4_6

3. M. Beckmann, N. Heilenkötter.
   Equivariant Neural Networks for Indirect Measurements.
   SIAM Journal on Mathematics of Data Science, 6(3), 2024.

   DOI: 10.1137/23M1582862
   online at: https://epubs.siam.org/doi/10.1137/23M1582862

4. M. Beckmann, A. Bhandari, M. Iske.
   Fourier-Domain Inversion for the Modulo Radon Transform.
   IEEE Transactions on Computational Imaging, 10, 2024.

   online at: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10499837

5. M. Beckmann, A. Bhandari, F. Krahmer.
   The Modulo Radon Transform: Theory, Algorithms and Applications.
   SIAM Journal on Imaging Sciences, 15(2):455-490, 2022.

   DOI: 10.1137/21M1424615