E
Erich Kobler
Researcher at Graz University of Technology
Publications - 38
Citations - 2022
Erich Kobler is an academic researcher from Graz University of Technology. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 8, co-authored 27 publications receiving 1202 citations. Previous affiliations of Erich Kobler include Johannes Kepler University of Linz.
Papers
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Journal ArticleDOI
Learning a variational network for reconstruction of accelerated MRI data.
Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.
Posted Content
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: 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 variational models with deep learning.
Journal ArticleDOI
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Florian Knoll,Kerstin Hammernik,Kerstin Hammernik,Erich Kobler,Thomas Pock,Thomas Pock,Michael P. Recht,Daniel K. Sodickson +7 more
TL;DR: In this paper, the authors evaluated the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing, and provided an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.
Book ChapterDOI
Variational Networks: Connecting Variational Methods and Deep Learning
TL;DR: Surprisingly, in numerical experiments on image reconstruction problems it turns out that giving up exact minimization leads to a consistent performance increase, in particular in the case of convex models.
Proceedings ArticleDOI
Total Deep Variation for Linear Inverse Problems
TL;DR: This paper proposes a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning and casts the learning problem as a discrete sampled optimal control problem, for which the adjoint state equations and an optimality condition are derived.