@inproceedings{81e3d47e0ec844359bf6d373109770ca,
title = "Dynamic Tomography Reconstruction via Low-Rank Modeling with a RED Spatial Prior",
abstract = "Indynamic imaging, a spatiotemporal object is reconstructed from its inconsistent and undersampled measurements obtained over time. Specifically in dynamic tomography, these measurements correspond to the projections of the time-varying object. In this study, we use the dynamic imaging algorithm RED-PSM, which combines the low-rank partially separable modeling (PSM) with the popular Regularization by Denoising (RED) framework and achieves impressive results in different dynamic scenarios, on the tomographic imaging of materials under dynamic mechanical stresses. We compare RED-PSM with a recent deep prior-based technique and with a PSM method with a simple total variation regularization, and demonstrate the advantages of RED-PSM.",
keywords = "Dynamic tomography, Low rank modeling, Regularization by denoising",
author = "Berk Iskender and Klasky, {Marc L.} and Yoram Bresler",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 ; Conference date: 10-12-2023 Through 13-12-2023",
year = "2023",
doi = "10.1109/CAMSAP58249.2023.10403435",
language = "English (US)",
series = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "506--510",
booktitle = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",
address = "United States",
}