期刊
2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
卷 -, 期 -, 页码 1961-1969出版社
IEEE
DOI: 10.1109/WACV.2019.00213
关键词
-
资金
- United States Air Force
- Defense Advanced Research Projects Agency [FA8750-16-C-0190]
Camera Response Functions (CRFs) map the irradiance incident at a sensor pixel to an intensity value in the corresponding image pixel. The nonlinearity of CRFs impact physics-based and low-level computer vision methods like de-blurring, photometric stereo, etc. In addition, CRFs have been used for forensics to identify regions of an image spliced in from a different camera. Despite its importance, the process of radiometrically calibrating a camera's CRF is significantly harder and less standardized than geometric calibration. Competing methods use different mathematical models of the CRF, some of which are derived from an outdated dataset. We present a new dataset of 178 CRFs from modern digital cameras, derived from 1565 camera review images available online, and use it to answer a series of questions about CRFs. Which mathematical models are best for CRF estimation? How have they changed over time? And how unique are CRFs from camera to camera?
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