Color Constancy Based on Local Reflectance Differences

Abstract

Color constancy is used to determine the actual surface color of the scene affected by illumination so that the captured image is more in line with the characteristics of human perception. The well-known Gray-Edge hypothesis states that the average edge difference in a scene is achromatic. Inspired by the Gray-Edge hypothesis, we propose a new illumination estimation method. Specifically, after analyzing three public datasets containing rich illumination conditions and scenes, we found that the ratio of the global sum of reflectance differences to the global sum of locally normalized reflectance differences is achromatic. Based on this hypothesis, we also propose an accurate color constancy method. The method was tested on four test datasets containing various illumination conditions (three datasets in a single-light environment and one dataset in a multi-light environment). The results show that the proposed method outperforms the state-of-the-art color constancy methods. Furthermore, we propose a new framework that can incorporate current mainstream statistics-based color constancy methods (Gray-World, Max-RGB, Gray-Edge, etc.) into the proposed framework.