Name
Abstract | ||
|---|---|---|
A camera is a good instrument for measuring scene radiance. However, to please the human eye, the resulting image brightness is not linear to the scene radiance, so solving the mapping function between scene radiance and image brightness is very important. We propose a Progressive-CRF-net for radiometric calibration. By stacking multiple networks and using the pre-trained weights, this approach can reduce the training time and reach better performance than that of previous work. Our experiments show a significant improvement based on PSNR and SSIM.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3230744.3230770 | SIGGRAPH Posters |
Field | DocType | ISBN |
Human eye,Computer vision,Radiometric calibration,Convolutional neural network,Computer science,Artificial intelligence,Brightness,Radiance,Stacking | Conference | 978-1-4503-5817-0 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Lung Kao | 1 | 0 | 0.34 |
| yusheng chen | 2 | 33 | 2.63 |
| Ming Ouhyoung | 3 | 1609 | 266.64 |