The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of realworld illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

We present a physics-based approach for illuminant color estimation of arbitrary images, which is explicitly designed for handling images with multiple illuminants. The majority of techniques that extract the illuminant color assume that the illumination is constant across the scene. This, however, is not often the case. We propose an illuminant-color estimation method which is based on robust local illuminant estimates. There are no assumptions on the number or type of illuminants. An illuminant color estimate is obtained independently from distinct image mini-regions. From these mini-regions a robust local illumination color is computed by consensus. These local estimates are then used in deriving the chromaticity of the dominant illuminants. Experiments on an established benchmark database of real-world images show that our technique performs comparably to uniform-illuminant estimation methods. Furthermore, extensive tests on real-world images show that we can reliably process mixed illuminant scenes.

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