Name
Abstract | ||
|---|---|---|
The topic of high dynamic range (HDR) tomography is starting to gain attention due to recent advances in the hardware technology. Registering high-intensity projections that exceed the dynamic range of the detector cause sensor saturation. Existing methods rely on the fusion of multiple exposures. In contrast, we propose a one-shot solution based on the Modulo Radon Transform (MRT). By exploiting the modulo non-linearity, the MRT encodes folded Radon Transform projections so that the resulting measurements do not saturate. Our recovery strategy is pivoted around a property we call compactly lambda-supported, which is motivated by practice; in many applications the object to be recovered is of finite extent and the measured quantity has approximately compact support. Our theoretical results are illustrated by numerical simulations with an open-access X-ray tomographic dataset and lead to substantial improvement in the HDR recovery problem. For instance, we report recovery of objects with projections 1000x larger in amplitude than the detector threshold. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190878 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Computational imaging, computer tomography, high dynamic range, Radon transform and sampling theory | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias W Beckmann | 1 | 0 | 1.01 |
| Felix Krahmer | 2 | 369 | 27.16 |
| Ayush Bhandari | 3 | 65 | 5.52 |