© J. Saba
Prof. Dr. Alexander Effland
Affiliations
- Institute for Applied Mathematics
Research topics
- 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.
Selected publications
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
© J. Saba
Prof. Dr. Alexander Effland