Computational Color Constancy with Spatial Correlations

Abstract

The color of a scene recorded by a trichromatic sensor varies with the spectral distribution of the illuminant. For recognition and many other applications, we seek to process these measurements to obtain a color representation that is unaffected by illumination changes. Achieving such color constancy is an ill-posed problem because both the spectral distribution of the illuminant and the scene reflectance are unknown. For the most part, methods have approached this problem by leveraging the statistics of individual pixel measurements, independent from their spatial contexts. In this work, we show that the strong spatial correlations that exist between measurements at neighboring image points encode useful information about the illuminant and should not be ignored. We develop a method to encode these correlations in a statistical model and exploit them for color constancy. The method is computationally efficient, allows for the incorporation of prior information about natural illuminants, and performs well when evaluated on a large database of natural images.