88 citations · 197 across the 19 of their papers we have counts for
7 papers · 1 filter
Augmented NETT Regularization of Inverse Problems
Daniel Obmann, Linh Nguyen, Johannes Schwab +1
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a…
Learned backprojection for sparse and limited view photoacoustic tomography
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Filtered backprojection (FBP) is an efficient and popular class of tomographic image reconstruction methods. In photoacoustic tomography, these algorithms are based on theoreticall…
Sparse synthesis regularization with deep neural networks
Daniel Obmann, Johannes Schwab, Markus Haltmeier
We propose a sparse reconstruction framework for solving inverse problems. Opposed to existing sparse regularization techniques that are based on frame representations, we train an…
NETT Regularization for Compressed Sensing Photoacoustic Tomography
Stephan Antholzer, Johannens Schwab, Johannes Bauer-Marschallinger +2
We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab,…
Deep learning versus -minimization for compressed sensing photoacoustic tomography
Stephan Antholzer, Johannes Schwab, Markus Haltmeier
We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particula…
Photoacoustic image reconstruction via deep learning
Stephan Antholzer, Johannes Schwab, Robert Nuster +1
Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these a…
Deep Learning of truncated singular values for limited view photoacoustic tomography
Johannes Schwab, Stephan Antholzer, Robert Nuster +2
We develop a data-driven regularization method for the severely ill-posed problem of photoacoustic image reconstruction from limited view data. Our approach is based on the regular…