Karl Schrader
Position: Research Assistant
E-mail: schrader -at- mia.uni-saarland.de
(please replace anti-spam -at- by @)
Address: Mathematical Image Analysis Group
Faculty of Mathematics and Computer Science,
Saarland University
Campus E1.7
66041 Saarbrücken, Germany
Office: Room 4.07
Building E1.7, Saarbrücken Campus
see also Contact
Research Area


Journal Papers

  1. T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
    Connections between Numerical Algorithms for PDEs and Neural Networks.
    Journal of Mathematical Imaging and Vision.
    Invited Paper.
    Also available as arXiv:2107.14742 [math.NA], 2023.
  2. T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
    Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods.
    Research in the Mathematical Sciences.
    Also available as arXiv:2108.13993 [cs.LG], revised March 2022.
  3. P. Peter, K. Schrader, T. Alt, J. Weickert:
    Deep Spatial and Tonal Optimisation for Homogeneous Diffusion Inpainting.
    Pattern Analysis and Applications, Vol. 26, No. 4, 1585-1600.
    Invited Paper.
    Also available as arXiv:2208.14371 [eess.IV], November 2023.
  4. Conference Papers

  5. T. Alt, P. Peter, J. Weickert, K. Schrader:
    Translating Numerical Concepts for PDEs into Neural Architectures.
    In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2021.
    Also available as arXiv:2103.15419 [math.NA], March 2021.
  6. K. Schrader, T. Alt, J. Weickert, M. Ertel:
    CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior.
    Proc. 10th European Workshop on Visual Information Processing (EUVIP 2022, Lisbon, Portugal, Sept. 2022), IEEE, 2022
    Also available as arXiv:2207.07921 [cs.CV], July 2022.
  7. K. Schrader, P. Peter, N. Kämper, J. Weickert:
    Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting.
    In L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
    Also available as arXiv:2303.10096 [eess.IV], March 2023.
  8. K. Schrader, J. Weickert, M. Krause:
    Anisotropic Diffusion Stencils: From Simple Derivations over Stability Estimates to ResNet Implementations.
    To appear.
    Also available as arXiv:2309.05575 [math.NA], September 2023.
  9. Theses

  10. K. Schrader:
    Translating Anisotropic Diffusion into Residual Networks.
    M.Sc. Thesis in Computer Science,
    Saarland University, Saarbrücken, Germany, October 2020.

  11. Courses

    Supervised Students