Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source

Nikola Banić, Sven Lončarić

2019

Abstract

Color constancy methods for removing the influence of illumination on object colors are divided into statistics-based and learning-based ones. The latter have low illumination estimation error, but only on images taken with the same sensor and in similar conditions as the ones used during training. For an image taken with an unknown sensor, a statistics-based method will often give higher accuracy than an untrained or specifically trained learning-based method because of its simpler assumptions not bounded to any specific sensor. The accuracy of a statistics-based method also depends on its parameter values, but for an image from an unknown source these values can be tuned only blindly. In this paper the blue shift assumption is proposed, which acts as a heuristic for choosing the optimal parameter values in such cases. It is based on real-world illumination statistics coupled with the results of a subjective user study and its application outperforms blind tuning in terms of accuracy. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Paper Citation

in Harvard Style

Banić N. and Lončarić S. (2019). Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 191-197. DOI: 10.5220/0007394101910197

in Bibtex Style

@conference{visapp19,
author={Nikola Banić and Sven Lončarić},
title={Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={191-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007394101910197},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source
SN - 978-989-758-354-4
AU - Banić N.
AU - Lončarić S.
PY - 2019
SP - 191
EP - 197
DO - 10.5220/0007394101910197
PB - SciTePress