Dongyoung Kim1, Jinwoo Kim1, Seonghyeon Nam2, Dongwoo Lee1, Yeonkyung Lee3,
Nahyup Kang3, Hyong-Euk Lee3, ByungIn Yoo3, Jae-Joon Han3, Seon Joo Kim1
1Yonsei University, 2York University, 3Samsung Advanced Institute of Technology
ICCV 2021
We introduce a Large Scale Multi-Illuminant (LSMI) Dataset that contains 7,486 images, captured with three different cameras on more than 2,700 scenes with two or three illuminants. For each image in the dataset, the new dataset provides not only the pixel-wise ground truth illumination but also the chromaticity of each illuminant in the scene and the mixture ratio of illuminants per pixel. Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination. Therefore, our dataset captures natural composition in the real-world setting with wide field-of-view, providing more extensive dataset compared to existing datasets for multi-illumination white balance. As conventional single illuminant white balance algorithms cannot be directly applied, we also apply per-pixel DNN-based white balance algorithm and show its effectiveness against using patch-wise white balancing. We validate the benefits of our dataset through extensive analysis including a user-study, and expect the dataset to make meaningful contribution for future work in white balancing.
*Images are converted to sRGB after WB for visibility
@inproceedings{kim2021large,
title={Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination},
author={Kim, Dongyoung and Kim, Jinwoo and Nam, Seonghyeon and Lee, Dongwoo and Lee, Yeonkyung and Kang, Nahyup and Lee, Hyong-Euk and Yoo, ByungIn and Han, Jae-Joon and Kim, Seon Joo},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
pages={2410--2419},
year={2021}
}
