Fast Fourier Color Constancy
Abstract
We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13-20% while being 250-3000 times faster. This unconventional approach introduces challenges regarding aliasing, directional statistics, and preconditioning, which we address. By producing a complete posterior distribution over illuminants instead of a single illuminant estimate, FFCC enables better training techniques, an effective temporal smoothing technique, and richer methods for error analysis. Our implementation of FFCC runs at 700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm.
Cite
Text
Barron and Tsai. "Fast Fourier Color Constancy." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.735Markdown
[Barron and Tsai. "Fast Fourier Color Constancy." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/barron2017cvpr-fast/) doi:10.1109/CVPR.2017.735BibTeX
@inproceedings{barron2017cvpr-fast,
title = {{Fast Fourier Color Constancy}},
author = {Barron, Jonathan T. and Tsai, Yun-Ta},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2017},
doi = {10.1109/CVPR.2017.735},
url = {https://mlanthology.org/cvpr/2017/barron2017cvpr-fast/}
}