上海科技大学知识管理系统

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 Competing methods use different mathematical models of the CRF, some of which are derived from an outdated dataset.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?