12 citations · 17 across the 4 of their papers we have counts for
8 papers
Computed Tomography Reconstruction using Generative Energy-Based Priors
Martin Zach, Erich Kobler, Thomas Pock
In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by…
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
Dominik Narnhofer, Alexander Effland, Erich Kobler +3
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However,…
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…
Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study
Elena A. Kaye, Emily A. Aherne, Cihan Duzgol +8
Purpose: To investigate feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a…
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…
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…