11 papers
Learned iterative networks: An operator learning perspective
Andreas Hauptmann, Ozan Ãktem
Learned image reconstruction has become a pillar in computational imaging and inverse problems. Among the most successful approaches are learned iterative networks, which are formu…
Geometric shape matching for recovering protein conformations from single-particle Cryo-EM data
Erik Jansson, Jonathan Krook, Klas Modin +1
We address recovery of the three-dimensional backbone structure of single polypeptide proteins from single-particle cryo-electron microscopy (Cryo-SPA) data. Cryo-SPA produces nois…
Motion-Enabled Tomography via Gaussian Mixture Models
Daniel Burrows, Can Evren Yarman, Ozan Ãktem
Recovering physical properties of objects in motion is a core task across scientific and industrial applications. When the relative motion between the object and the sensing appara…
Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction
Jonathan Krook, Axel Janson, Joakim Andén +2
We present a geometry-aware method for heterogeneous single-particle cryogenic electron microscopy (cryo-EM) reconstruction that predicts atomic backbone conformations. To incorpor…
Improving the Generalisation of Learned Reconstruction Frameworks
Emilien Valat, Ozan Ãktem
Ensuring proper generalization is a critical challenge in applying data-driven methods for solving inverse problems in imaging, as neural networks reconstructing an image must perf…
Sparse View Tomographic Reconstruction of Elongated Objects using Learned Primal-Dual Networks
Buda BajiÄ, Johannes A. J. Huber, Benedikt Neyses +2
In the wood industry, logs are commonly quality screened by discrete X-ray scans on a moving conveyor belt from a few source positions. Typically, the measurements are obtained in…