11 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…
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…
Locally-Supervised Global Image Restoration
Benjamin Walder, Daniel Toader, Robert Nuster +5
We address the problem of image reconstruction from incomplete measurements, encompassing both upsampling and inpainting, within a learning-based framework. Conventional supervised…
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…
Full Field Inversion of the Attenuated Wave Equation: Theory and Numerical Inversion
Ngoc Do, Markus Haltmeier, Richard Kowar +2
Standard photoacoustic tomography (PAT) provides data that consist of time-dependent signals governed by the wave equation, which are measured on an observation surface. In contras…
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…
Data-driven Morozov regularization of inverse problems
Markus Haltmeier, Richard Kowar, Markus Tiefenthaler
The solution of inverse problems is crucial in various fields such as medicine, biology, and engineering, where one seeks to find a solution from noisy observations. These problems…