88 citations · 197 across the 19 of their papers we have counts for
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Big in Japan: Regularizing networks for solving inverse problems
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis f…
Deep Null Space Learning for Inverse Problems: Convergence Analysis and Rates
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Recently, deep learning based methods appeared as a new paradigm for solving inverse problems. These methods empirically show excellent performance but lack of theoretical justific…
NETT: Solving Inverse Problems with Deep Neural Networks
Housen Li, Johannes Schwab, Stephan Antholzer +1
Recovering a function or high-dimensional parameter vector from indirect measurements is a central task in various scientific areas. Several methods for solving such inverse proble…
Real-time photoacoustic projection imaging using deep learning
Johannes Schwab, Stephan Antholzer, Robert Nuster +1
Photoacoustic tomography (PAT) is an emerging and non-invasive hybrid imaging modality for visualizing light absorbing structures in biological tissue. The recently invented PAT sy…