Robust and User Friendly 3D Re-Construction of Neutron Tomographic Images
Abstract
Three-dimensional (3D) reconstruction of neutron tomographic projection images is an important tool for research on animal and plant tissues. Neutron scattering images contain impulsive noise caused by background cosmic gamma radiation that can significantly affect the reconstruction quality. Common de-noising methods are computationally efficient, but may also blur edges in the signal reducing the quality of reconstruction and may require careful parameter selection. Moreover, prior to reconstruction we must correct for rotation axis misalignment during data acquisition and suppress statistical noise due to variations in the neutron source. Currently many of these steps require manual intervention and parameter selection to maximize reconstruction quality. We have developed a more automatic algorithm which performs comparably to the semi-automatic state-of-the-art process.
Cite
Text
Song et al. "Robust and User Friendly 3D Re-Construction of Neutron Tomographic Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018. doi:10.1109/WACV.2018.00107Markdown
[Song et al. "Robust and User Friendly 3D Re-Construction of Neutron Tomographic Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018.](https://mlanthology.org/wacv/2018/song2018wacv-robust/) doi:10.1109/WACV.2018.00107BibTeX
@inproceedings{song2018wacv-robust,
title = {{Robust and User Friendly 3D Re-Construction of Neutron Tomographic Images}},
author = {Song, Hao and Eramian, Mark G. and Hallin, Emil and Leyeza, Blanche and Arnison, Paul G. and Rogge, Ronald},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2018},
pages = {930-938},
doi = {10.1109/WACV.2018.00107},
url = {https://mlanthology.org/wacv/2018/song2018wacv-robust/}
}