CSF-Net: Color Space Fusion Network for Color Constancy

Color constancy is the ability of the human visual system to correctly perceive colors in a scene under different illuminants. To mimic this ability, computational color constancy has been proposed to estimate illuminant color and correct the color bias for a given image. Existing computational color constancy approaches focus on the widely used RGB color space. However, RGB color space could not explicitly reflect color characteristics, so it is necessary to investigate other color spaces that better express color features. HSV color space separates the color channel from others, and hence helps us to distinguish different colors. In order to better predict illuminant colors, we design a color space fusion network CSF-Net that integrates the RGB and HSV color spaces at both the feature level and the prediction level. At the feature level, color features extracted from RGB and HSV are combined to estimate an illuminant color. At the prediction level, two illuminant colors are predicted respectively by the color features from RGB and HSV. By merging all the three predicted colors, the illuminant color is finally predicted. Experiments conducted on the public Gehler-Shi and NUS 8-camera datasets show that the proposed CSF-Net performs better than most of the prior works in illuminant color estimation and achieves better color correction in the case of complex illumination.