6 citations · 6 across the 6 of their papers we have counts for
6 papers
Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
Meira Iske, Carola-Bibiane Schönlieb
Many normalizing flow architectures impose regularity constraints, yet their distributional approximation properties are not fully characterized. We study the expressivity of bi-Li…
A remark on an error analysis for classical and learned Tikhonov regularization schemes
Arne Behrens, Meira Iske, Ming Jiang +2
This paper presents an error analysis of classical and learned Tikhonov regularization schemes for inverse problems. We first demonstrate, both theoretically and numerically, that…
Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging
Meira Iske, Hannes Albers, Tobias Knopp +1
Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-tim…
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…
Fourier-Domain Inversion for the Modulo Radon Transform
Matthias Beckmann, Ayush Bhandari, Meira Iske
Inspired by the multiple-exposure fusion approach in computational photography, recently, several practitioners have explored the idea of high dynamic range (HDR) X-ray imaging and…
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…