Analyzing Modern Camera Response Functions
Abstract
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 Competing methods use different mathematical models of the CRF, some of which are derived from an outdated dataset.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?
Cite
Text
Chen et al. "Analyzing Modern Camera Response Functions." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00213Markdown
[Chen et al. "Analyzing Modern Camera Response Functions." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/chen2019wacv-analyzing/) doi:10.1109/WACV.2019.00213BibTeX
@inproceedings{chen2019wacv-analyzing,
title = {{Analyzing Modern Camera Response Functions}},
author = {Chen, Can and McCloskey, Scott and Yu, Jingyi},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2019},
pages = {1961-1969},
doi = {10.1109/WACV.2019.00213},
url = {https://mlanthology.org/wacv/2019/chen2019wacv-analyzing/}
}