Name
Abstract | ||
|---|---|---|
Capturing hyperspectral images requires expensive and specialized hardware that is not readily accessible to most users. Digital cameras, on the other hand, are significantly cheaper in comparison and can be easily purchased and used. In this paper, we present a framework for reconstructing hyperspectral images by using multiple consumer-level digital cameras. Our approach works by exploiting the different spectral sensitivities of different camera sensors. In particular, due to the differences in spectral sensitivities of the cameras, different cameras yield different RGB measurements for the same spectral signal. We introduce an algorithm that is able to combine and convert these different RGB measurements into a single hyperspectral image for both indoor and outdoor scenes. This camera-based approach allows hyperspectral imaging at a fraction of the cost of most existing hyperspectral hardware. We validate the accuracy of our reconstruction against ground truth hyperspectral images (using both synthetic and real cases) and show its usage on relighting applications. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.270 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer vision,Full spectral imaging,Computer graphics (images),Image sensor,Computer science,Hyperspectral imaging,Ground truth,Artificial intelligence,RGB color model | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 2 |
PageRank | References | Authors |
0.37 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seoung Wug Oh | 1 | 23 | 7.12 |
| Michael S. Brown | 2 | 2122 | 129.13 |
| Marc Pollefeys | 3 | 7671 | 475.90 |
| Seon Joo Kim | 4 | 455 | 31.34 |