150 citations · 480 across the 12 of their papers we have counts for
12 papers
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
Theophil Trippe, Martin Genzel, Jan Macdonald +1
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build…
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel, Ingo Gühring, Jan Macdonald +1
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect acc…
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
Martin Genzel, Jan Macdonald, Maximilian März
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable"). The task is to recover…
Solving Inverse Problems With Deep Neural Networks -- Robustness Included?
Martin Genzel, Jan Macdonald, Maximilian März
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical field…
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20)
Martin Genzel, Maximilian März, Robert Seidel
This paper investigates total variation minimization in one spatial dimension for the recovery of gradient-sparse signals from undersampled Gaussian measurements. Recently establis…
Interval Neural Networks as Instability Detectors for Image Reconstructions
Jan Macdonald, Maximilian März, Luis Oala +1
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks. Although neural networks often empirically…
Interval Neural Networks: Uncertainty Scores
Luis Oala, Cosmas Heiß, Jan Macdonald +3
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network. T…
Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery
Martin Genzel, Maximilian März, Robert Seidel
This paper investigates total variation minimization in one spatial dimension for the recovery of gradient-sparse signals from undersampled Gaussian measurements. Recently establis…
Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
T. A. Bubba, G. Kutyniok, M. Lassas +4
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same…
-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed?
Martin Genzel, Gitta Kutyniok, Maximilian März
This paper investigates the problem of signal estimation from undersampled noisy sub-Gaussian measurements under the assumption of a cosparse model. Based on generalized notions of…
Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting
Jackie Ma, Maximilian März, Stephanie Funk +4
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampe…
A multilevel based reweighting algorithm with joint regularizers for sparse recovery
Jackie Ma, Maximilian März
Sparsity is one of the key concepts that allows the recovery of signals that are subsampled at a rate significantly lower than required by the Nyquist-Shannon sampling theorem. Our…