no code implementations • 26 Feb 2024 • Nadja Gruber, Johannes Schwab, Elke Gizewski, Markus Haltmeier
Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing.
1 code implementation • 19 Sep 2023 • Nadja Gruber, Johannes Schwab, Noémie Debroux, Nicolas Papadakis, Markus Haltmeier
We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image.
no code implementations • 4 Feb 2023 • Nadja Gruber, Johannes Schwab, Sebastien Court, Elke Gizewski, Markus Haltmeier
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks.
1 code implementation • 9 Feb 2022 • Nadja Gruber, Johannes Schwab, Sebastien Court, Elke Gizewski, Markus Haltmeier
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties.
no code implementations • 20 Apr 2020 • Daniel Obmann, Linh Nguyen, Johannes Schwab, Markus Haltmeier
We propose a sparse reconstruction framework (aNETT) for solving inverse problems.
no code implementations • 1 Feb 2020 • Daniel Obmann, Johannes Schwab, Markus Haltmeier
For these deep learning methods, however, a solid theoretical foundation in the form of reconstruction guarantees is missing.
no code implementations • 8 Aug 2019 • Daniel Obmann, Linh Nguyen, Johannes Schwab, Markus Haltmeier
We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems.
no code implementations • 28 Feb 2018 • Housen Li, Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Our theoretical results and framework are different from any previous work using neural networks for solving inverse problems.
no code implementations • 15 Apr 2017 • Stephan Antholzer, Markus Haltmeier, Johannes Schwab
In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data.