11 citations · 18 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Invertible residual networks in the context of regularization theory for linear inverse problems
Clemens Arndt, Alexander Denker, Sören Dittmer +5
Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks. These data-driven reconstruction methods often follow a two-step sch…
Error analysis for filtered back projection reconstructions in Besov spaces
Matthias Beckmann, Peter Maass, Judith Nickel
Filtered back projection (FBP) methods are the most widely used reconstruction algorithms in computerized tomography (CT). The ill-posedness of this inverse problem allows only an…