IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction
Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, Wolfgang Heidrich
9/2/2021
Keywords: Science & Engineering
Venue: ICCV 2021
Bibtex:
@inproceedings{zang2021intratomo,
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
year = {2021},
author = {Guangming Zang and Ramzi Idoughi and Rui Li and Peter Wonka and Wolfgang Heidrich},
publisher = {IEEE},
title = {IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction},
entrytype = {inproceedings},
id = {zang2021intratomo}
}
Abstract
We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems, in the Computed Tomography (CT) context. IntraTomo is composed of two core modules: a novel sinogram prediction module and a geometry refinement module, which are applied iteratively. In the first module, the unknown density field is represented as a continuous and differentiable function, parameterized by a deep neural network. This network is learned, in
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