Automatic white balance based on adaptive feature selection with standard illuminants

Abstract

Automatic white balance is a key function to remove undesirable color cast in digital cameras. Assumption-based methods (e.g. gray world method, white patch method) are widely used for the AWB process of digital cameras because of their low computational costs. However, they are highly dependent on the validity of their assumption. To solve this problem, we propose an adaptive feature extraction scheme for finding neutral color. The proposed method uses three features which are complementary and computationally effective. Moreover, we use the standard illuminants for evaluating an estimated illuminant not only to improve the accuracy of illuminant estimation, but also to provide the AWB skip mode. The experimental results show that the proposed method outperforms conventional assumption-based methods and arrives at reasonable color compensation over a wide range of scenes.