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arXiv · 2109.00863

Generative Models for Multi-Illumination Color Constancy

Partha Das·Yang Liu·Sezer Karaoglu·Theo Gevers
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Abstract

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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Partha Das, Yang Liu, Sezer Karaoglu, Theo Gevers. 2021-09-02. Generative Models for Multi-Illumination Color Constancy. https://arxiv.org/abs/2109.00863

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