88 citations · 197 across the 19 of their papers we have counts for
26 papers
Design Choices in Splitting-Based Self-Supervised Sparse-View CT Reconstruction
Nadja Gruber, Lukas Neumann, Ander Biguri +3
Self-supervised data splitting has emerged as a promising paradigm for sparse-view CT reconstruction, enabling training from incomplete measurements without fully sampled ground tr…
HyDRA: Hybrid Denoising Regularization for Measurement-Only DEQ Training
Markus Haltmeier, Lukas Neumann, Nadja Gruber +2
Solving image reconstruction problems of the form \(\mathbf{A} \mathbf{x} = \mathbf{y}\) remains challenging due to ill-posedness and the lack of large-scale supervised datasets. D…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise
Nadja Gruber, Johannes Schwab, Markus Haltmeier +3
We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the…
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Nadja Gruber, Johannes Schwab, Elke Gizewski +1
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. In sparse-view C…
Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
Nadja Gruber, Johannes Schwab, Noémie Debroux +2
We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with se…
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a rec…
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determin…
Realizability of tropical pluri-canonical divisors
Felix Röhrle, Johannes Schwab
Consider a pair consisting of an abstract tropical curve and an effective divisor from the linear system associated to times the canonical divisor for $k \in \mathbb{Z}_{\geq 1…
Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition
Andrea Ebner, Jürgen Frikel, Dirk Lorenz +2
The characteristic feature of inverse problems is their instability with respect to data perturbations. In order to stabilize the inversion process, regularization methods have to…
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…
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…
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…
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…