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Shared Prior Learning of Energy-Based Models for Image Reconstruction
Thomas Pinetz, Erich Kobler, Thomas Pock +1
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-bas…
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn +3
In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for re…
PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps
Thomas Grandits, Simone Pezzuto, Jolijn M. Lubrecht +3
Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is ther…
Total Deep Variation: A Stable Regularizer for Inverse Problems
Erich Kobler, Alexander Effland, Karl Kunisch +1
Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts t…
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov +2
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main diffi…
Total Deep Variation for Linear Inverse Problems
Erich Kobler, Alexander Effland, Karl Kunisch +1
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term. In this paper, we propose a novel…