A study of non-diagonal models for image white balance

White balance is an algorithm proposed to mimic the color constancy mechanism of human perception. However, as shown by its name, current white balance algorithms only promise to correct the color shift of gray tones to correct positions; for other color values, white balance algorithms process them as gray tones and therefore produce undesired color biases. To improve the color prediction of white balance algorithms, in this paper, we propose a 3-parameter nondiagonal model, named as PCA-CLSE, for white balance. Unlike many previous researches which use the von Kries diagonal model for color prediction, we proposed applying a non-diagonal model for color correction which aimed to minimize the color biases while keeping the balance of white color. In our method, to reduce the color biases, we proposed a PCA-based training method to gain extra information for analysis and built a mapping model between illumination and non-diagonal transformation matrices. While a color-biased image is given, we could estimate the illumination and dynamically determine the illumination-dependent transformation matrix to correct the color-biased image. Our evaluation shows that the proposed PCA-CLSE model can efficiently reduce the color biases.