26 citations · 49 across the 11 of their papers we have counts for
14 papers
MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction
Antonio Ortiz-Gonzalez, Erich Kobler, Lukas Schletter +1
Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. E…
Product of Gaussian Mixture Diffusion Models
Martin Zach, Erich Kobler, Antonin Chambolle +1
In this work we tackle the problem of estimating the density of a random variable by successive smoothing, such that the smoothed random variable fulfills the d…
Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs
Thomas Pinetz, Erich Kobler, Robert Haase +3
Today Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic Resonance Imaging (MRI) for diagnosing various diseases. However, GBCAs are expensive and may accumulate…
Learning Gradually Non-convex Image Priors Using Score Matching
Erich Kobler, Thomas Pock
In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization. We show that for sufficiently large noise…
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
Martin Zach, Thomas Pock, Erich Kobler +1
In this work we tackle the problem of estimating the density of a random variable by successive smoothing, such that the smoothed random variable fulfills $(\partial_…
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…
Image Morphing in Deep Feature Spaces: Theory and Applications
Alexander Effland, Erich Kobler, Thomas Pock +2
This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic…
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
Alexander Effland, Erich Kobler, Karl Kunisch +1
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped b…
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler +4
Purpose: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of vari…