3 citations · 3 across the 10 of their papers we have counts for
10 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…
Denoising ICF Images with Multiplicative Uniform Noise: A Self-Supervised Study Based on the Log-Domain Noisier2Inverse Framework
Gyeongha Hwang, Bradley Thomas Wolfe, Naima Naheed
This paper documents the implementation and evaluation of a self-supervised denoising framework on Inertial Confinement Fusion (ICF) images corrupted by Multiplicative Uniform nois…
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…
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise
Nadja Gruber, Johannes Schwab, Markus Haltmeier +3
We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the…
ECALL: Expectation-calibrated learning for unsupervised blind deconvolution
Markus Haltmeier, Gyeongha Hwang
Blind deconvolution aims to recover an original image from a blurred version in the case where the blurring kernel is unknown. It has wide applications in diverse fields such as as…
Convergence analysis of equilibrium methods for inverse problems
Daniel Obmann, Gyeongha Hwang, Markus Haltmeier
Solving inverse problems \(Ax = y\) is central to a variety of practically important fields such as medical imaging, remote sensing, and non-destructive testing. The most successfu…