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. P. Peter, K. Schrader, T. Alt, J. Weickert:
    Deep Spatial and Tonal Optimisation for Homogeneous Diffusion Inpainting.
    To appear in Pattern Analysis and Applications.
    Invited Paper.
    Also available as arXiv:2208.14371 [eess.IV], revised September 2022.
  2. 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], revised March 2022.
  3. 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.
  4. Conference Papers

  5. 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.
  6. K. Schrader, T. Alt, J. Weickert, M. Ertel:
    CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior.
    In Proceedings of the 10th European Workshop on Visual Information Processing, Lisbon, 2022.
    Also available as arXiv:2207.07921 [cs.CV], July 2022.
  7. 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.
  8. Technical Reports

  9. K. Schrader, J. Weickert, M. Krause:
    Anisotropic Diffusion Stencils: From Simple Derivations over Stability Estimates to ResNet Implementations.
    arXiv:2309.05575 [math.NA], September 2023.
  10. Theses

  11. K. Schrader:
    Translating Anisotropic Diffusion into Residual Networks.
    M.Sc. Thesis in Visual Computing,
    Saarland University, Saarbrücken, Germany, October 2020.

  12. Courses