Auto white balance by surface reflection decomposition

Auto white balance (AWB) is an important operation in color imaging applications. Most existing AWB algorithms rely on some physical features and statistical properties of natural scenes. However, the AWB algorithms using statistical properties are sensitive to the statistics of the scene contents. Therefore, it is highly desirable to find physical features that are more robust and relatively insensitive to scene contents. In this paper, we propose such physical features based on surface reflection decomposition. Light reflection from most object surfaces can be decomposed into a specular component and a diffuse component. Instead of trying to find the common axis of the color planes as in past algorithms, we estimate the illuminant chromaticity by searching through the light source candidates to find the one that will best cancel the specular components. We provide two formulations: the minimum projected area algorithm and the minimum total variation algorithm for estimation of the scene-illuminant chromaticity. Both show very favorable results compared with other published algorithms.