30 citations · 40 across the 8 of their papers we have counts for
18 papers
Regularization of Inverse Problems by Neural Networks
Markus Haltmeier, Linh V. Nguyen
Inverse problems arise in a variety of imaging applications including computed tomography, non-destructive testing, and remote sensing. The characteristic features of inverse probl…
Sparse aNETT for Solving Inverse Problems with Deep Learning
Daniel Obmann, Linh Nguyen, Johannes Schwab +1
We propose a sparse reconstruction framework (aNETT) for solving inverse problems. Opposed to existing sparse reconstruction techniques that are based on linear sparsifying transfo…
Data-consistent neural networks for solving nonlinear inverse problems
Yoeri E. Boink, Markus Haltmeier, Sean Holman +1
Data assisted reconstruction algorithms, incorporating trained neural networks, are a novel paradigm for solving inverse problems. One approach is to first apply a classical recons…
The conical Radon transform with vertices on triple lines
Markus Haltmeier, Sunghwan Moon
We study the inversion of the conical Radon which integrates a function in three-dimensional space from integrals over circular cones. The conical Radon recently got significant at…
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
Andreas Kofler, Marc Dewey, Tobias Schaeffter +2
In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shall…
Deep synthesis regularization of inverse problems
Daniel Obmann, Johannes Schwab, Markus Haltmeier
Recently, a large number of efficient deep learning methods for solving inverse problems have been developed and show outstanding numerical performance. For these deep learning met…