Constrained low-rank gamut completion for robust illumination estimation

Abstract. Illumination estimation is an important component of color constancy and automatic white balancing. According to recent survey and evaluation work, the supervised methods with a learning phase are competitive for illumination estimation. However, the robustness and performance of any supervised algorithm suffer from an incomplete gamut in training image sets because of limited reflectance surfaces in a scene. In order to address this problem, we present a constrained low-rank gamut completion algorithm, which can replenish gamut from limited surfaces in an image, for robust illumination estimation. In the proposed algorithm, we first discuss why the gamut completion is actually a low-rank matrix completion problem. Then a constrained low-rank matrix completion framework is proposed by adding illumination similarities among the training images as an additional constraint. An optimization algorithm is also given out by extending the augmented Lagrange multipliers. Finally, the completed gamut based on the proposed algorithm is fed into the support vector regression (SVR)-based illumination estimation method to evaluate the effect of gamut completion. The experimental results on both synthetic and real-world image sets show that the proposed gamut completion model not only can effectively improve the performance of the original SVR method but is also robust to the surface insufficiency in training samples.