Search Results for author: Erich Kobler

Found 7 papers, 2 papers with code

Shared Prior Learning of Energy-Based Models for Image Reconstruction

no code implementations12 Nov 2020 Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland

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-based learning, a patch-based Wasserstein loss functional, and shared prior learning.

Image Reconstruction

Total Deep Variation: A Stable Regularizer for Inverse Problems

1 code implementation15 Jun 2020 Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock

In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer.

Total Deep Variation for Linear Inverse Problems

1 code implementation CVPR 2020 Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock

Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term.

Image Reconstruction Image Restoration

An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration

no code implementations19 Jul 2019 Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock

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 before converging to a stationary point.

Deblurring Image Deblurring +2

Learning a Variational Network for Reconstruction of Accelerated MRI Data

no code implementations3 Apr 2017 Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, Florian Knoll

Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.

Image Reconstruction Learning Theory

Cannot find the paper you are looking for? You can Submit a new open access paper.