Name
Abstract | ||
|---|---|---|
We present 3DeepCT, a deep neural network for computed tomography, which performs 3D reconstruction of scattering volumes from multi-view images. The architecture is dictated by the stationary nature of atmospheric cloud fields. The task of volumetric scattering tomography aims at recovering a volume from its 2D projections. This problem has been approached by diverse inverse methods based on signal processing and physics models. However, such techniques are typically iterative, exhibiting a high computational load and a long convergence time. We show that 3DeepCT outperforms physics-based inverse scattering methods, in accuracy, as well as offering orders of magnitude improvement in computational run-time. We further introduce a hybrid model that combines 3DeepCT and physics-based analysis. The resultant hybrid technique enjoys fast inference time and improved recovery performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCV48922.2021.00562 | ICCV |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yael Sde-Chen | 1 | 0 | 0.34 |
| Yoav Y. Schechner | 2 | 629 | 58.12 |
| vadim holodovsky | 3 | 1 | 1.37 |
| Eshkol Eytan | 4 | 0 | 0.34 |