5 papers
ELIPPS: Exact Learning for Inverse Problems from Partial Self-supervision
Benjamin Walder, Markus Haltmeier, Lukas Neumann +2
In undersampled inverse problems (such as sparse-view computed tomography), only a small number of measurements are collected, which reduces radiation exposure and acquisition time…
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…
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Markus Haltmeier, Lukas Neumann, Nadja Gruber +1
Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-truth images that are often unav…
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…
Derivative-Free iterative One-Step Reconstruction for Multispectral CT
Thomas Prohaszka, Lukas Neumann, Markus Haltmeier
Image reconstruction in Multispectral Computed Tomography (MSCT) requires solving a challenging nonlinear inverse problem, commonly tackled via iterative optimization algorithms. E…