Generative Models for Multi-Illumination Color Constancy

Partha Das, Yang Liu, Sezer Karaoglu, Theo Gevers; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 1194-1203


In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color constancy method. GANs are exploited to model the illumination estimation problem as an image-to-image domain translation problem. Additionally, a novel multi-illumination data augmentation method is proposed. Experiments on single and multi-illumination datasets show that our methods outperform sota methods.

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@InProceedings{Das_2021_ICCV, author = {Das, Partha and Liu, Yang and Karaoglu, Sezer and Gevers, Theo}, title = {Generative Models for Multi-Illumination Color Constancy}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1194-1203} }