Alexander Effland
Prof. Dr. Alexander Effland
  • Institute for Applied Mathematics
  • image processing
  • computer vision
Alexander Effland's research interests include mathematical image processing/computer vision (variational methods, PDE-based approaches, machine learning, deep learning), mathematics of deep learning, shape analysis, and discrete Riemannian geometry. These methods are frequently applied to problems in immunology, cardiology, or radiology.
Ausgewählte Publikationen

Pinetz T, Kobler E, Pock T, Effland A (2021) Shared Prior Learning of Energy-Based Models for Image Reconstruction. SIAM J. Imaging Sci. 14(4):1706-1748.

Effland A, Kobler E, Kunisch K, Pock T (2020) Variational Networks: An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration. J. Math. Imaging Vis. 62:396-416

Effland A, Neumayer S, Rumpf M (2020) Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds. SIAM J. Imaging Sci. 13(2):557-588

Kobler E*, Effland A*, Kunisch K, Pock T (2020) Total Deep Variation for Linear Inverse Problems. CVPR:7549-7558

Alexander Effland
Prof. Dr. Alexander Effland
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