Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

Dongyoung Kim, Jinwoo Kim, Seonghyeon Nam, Dongwoo Lee, Yeonkyung Lee, Nahyup Kang, Hyong Euk Lee, Byung In Yoo, Jae Joon Han, Seon Joo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2390-2399
Number of pages10
ISBN (Electronic)9781665428125
DOIs
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 2021 Oct 112021 Oct 17

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Country/TerritoryCanada
CityVirtual, Online
Period21/10/1121/10/17

Bibliographical note

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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