Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WACV.2018.00107 | 2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) |
Field | DocType | ISSN |
Iterative reconstruction,Computer vision,Neutron,Neutron source,Statistical noise,Neutron scattering,Computer science,Data acquisition,Artificial intelligence,Detector,Radiation | Conference | 2472-6737 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Song | 1 | 50 | 12.34 |
| Mark G Eramian | 2 | 26 | 7.96 |
| Emil Hallin | 3 | 0 | 0.34 |
| Blanche Leyeza | 4 | 0 | 0.34 |
| Paul G. Arnison | 5 | 0 | 0.34 |
| Ronald Rogge | 6 | 0 | 0.34 |