217 citations · 323 across the 12 of their papers we have counts for
19 papers · 1 filter
Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
Laurenz Nagler, Martin Zach, Thomas Pock
Recently, diffusion models have attracted considerable attention for magnetic resonance image reconstruction due to their high sample quality. However, most existing methods rely o…
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
Filip Ilic, He Zhao, Thomas Pock +1
Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through o…
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…
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…