6 papers · 1 filter
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…
Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse
Markus Haltmeier, Nadja Gruber, Gyeongha Hwang
Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution. A central task is image reco…
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…
Data-Consistent Learning of Inverse Problems
Markus Haltmeier, Gyeongha Hwang
Inverse problems are inherently ill-posed, suffering from non-uniqueness and instability. Classical regularization methods provide mathematically well-founded solutions, ensuring s…
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…