Approximate Cross Channel Color Mapping from Sparse Color Correspondences
Abstract
We propose a color mapping method that compensates color differences between images having a common semantic content such as multiple views of a scene taken from different viewpoints. A so-called color mapping model is usually estimated from color correspondences selected from those images. In this work, we introduce a color mapping that model color change in two steps: first, nonlinear, channel-wise mapping, second, linear, cross-channel mapping. Additionally, unlike many state of the art methods, we estimate the model from sparse matches and do not require dense geometric correspondences. We show that well known cross-channel color change can be estimated from sparse color correspondence. Quantitative and visual benchmark tests show good performance compared to recent methods in literature.
Cite
Text
Faridul et al. "Approximate Cross Channel Color Mapping from Sparse Color Correspondences." IEEE/CVF International Conference on Computer Vision Workshops, 2013. doi:10.1109/ICCVW.2013.118Markdown
[Faridul et al. "Approximate Cross Channel Color Mapping from Sparse Color Correspondences." IEEE/CVF International Conference on Computer Vision Workshops, 2013.](https://mlanthology.org/iccvw/2013/faridul2013iccvw-approximate/) doi:10.1109/ICCVW.2013.118BibTeX
@inproceedings{faridul2013iccvw-approximate,
title = {{Approximate Cross Channel Color Mapping from Sparse Color Correspondences}},
author = {Faridul, Hasan Sheikh and Stauder, Jürgen and Kervec, Jonathan and Trémeau, Alain},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2013},
pages = {860-867},
doi = {10.1109/ICCVW.2013.118},
url = {https://mlanthology.org/iccvw/2013/faridul2013iccvw-approximate/}
}