A Novel Automatic White Balance Method for Color Constancy Under Different Color Temperatures

The spectrum distribution of the light source between scenes is usually uniform in all scenes lit by a given light source per the color constancy assumption. Nonuniform spectrum distribution results in pictures with different color temperatures in different lighting conditions. With the conventional automated white balance method, the results under different degrees of color cast are inconsistent. To resolve this, a new automated white balance algorithm is proposed to achieve uniformity in images with color cast caused by color temperature variations. The proposed method features the selection of near-white points through automated regression and the dynamic adjustment of the color components’ weights. Finally, white balance is implemented according to the selected near-white points with various percentages. The proposed method can effectively select the appropriate white reference point of the image scene under different color temperatures, so that the adjusted results of the method under different color temperatures are close. The first major contribution of this study is the minimization of differences of continual color temperature images with different color temperatures and the achievement of favorable visual effects. The second contribution is the superior outcomes yielded by the proposed method in the visualization and quantification of data compared with those from the existing methods. The experimental results demonstrate that this method yields favorable results in the improvement of experiments with continual different color temperatures. The images at various color temperatures are adjusted to approach real-world color temperatures. In addition, this method provides superior color constancy preservation to other methods.