3 papers
math.NA2024
Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties
Clemens Arndt, Judith Nickel
Learning-based methods have demonstrated remarkable performance in solving inverse problems, particularly in image reconstruction tasks. Despite their success, these approaches oft…
math.NA2024
Optimized filter functions for filtered back projection reconstructions
Matthias Beckmann, Judith Nickel
The method of filtered back projection (FBP) is a widely used reconstruction technique in X-ray computerized tomography (CT), which is particularly important in clinical diagnostic…
math.NA2023
Bayesian view on the training of invertible residual networks for solving linear inverse problems
Clemens Arndt, Sören Dittmer, Nick Heilenkötter +3
Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often r…