detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2406.01299
Tema
Electrical Engineering and Systems... Computer Science - Computer Vision...Autor
Arratia, Pablo Ehrhardt, Matthias Kreusser, LisaCategoría
Computer Science
Año
2024
fecha de cotización
5/6/2024
Palabras clave
timeResumen
Image reconstruction for dynamic inverse problems with highly undersampled data poses a major challenge: not accounting for the dynamics of the process leads to a non-realistic motion with no time regularity.
Variational approaches that penalize time derivatives or introduce motion model regularizers have been proposed to relate subsequent frames and improve image quality using grid-based discretization.
Neural fields offer an alternative parametrization of the desired spatiotemporal quantity with a deep neural network, a lightweight, continuous, and biased towards smoothness representation.
The inductive bias has been exploited to enforce time regularity for dynamic inverse problems resulting in neural fields optimized by minimizing a data-fidelity term only.
In this paper we investigate and show the benefits of introducing explicit PDE-based motion regularizers, namely, the optical flow equation, in 2D+time computed tomography for the optimization of neural fields.
We also compare neural fields against a grid-based solver and show that the former outperforms the latter.
Arratia, Pablo,Ehrhardt, Matthias,Kreusser, Lisa, 2024, Enhancing Dynamic CT Image Reconstruction with Neural Fields Through Explicit Motion Regularizers
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