144 citations · 382 across the 39 of their papers we have counts for
42 papers
Fast reconstruction of tensor tomographic X-ray scattering data for real-time applications
André M. Antunes, Daniël M. Pelt, K. Joost Batenburg
X-ray scattering tensor tomography reveals nanoscale structural orientation in 3D, but its reliance on slow iterative reconstruction limits real-time use. We introduce an extension…
Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Tianyuan Wang, Daniël M. Pelt, Felix Lucka +2
Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to…
Efficient Flow Matching for Sparse-View CT Reconstruction
Jiayang Shi, Lincen Yang, Zhong Li +3
Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed in…
DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many prac…
Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
Tarik Zaciragic, Aske Plaat, K. Joost Batenburg
In the game of poker, being unpredictable, or bluffing, is an essential skill. When humans play poker, they bluff. However, most works on computer-poker focus on performance metric…
Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging
Mathieu Manni, Dmitry Karpov, K. Joost Batenburg +2
We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction quality for noisy acquisitions among unsup…