Improved grey world color correction method based on weighted gain coefficients

Grey world algorithm is a simple but widely used global white balance method for color cast images. However, this algorithm only assumes that the mean values of the R, G, and B components tend to be equal, which may lead to false alarms in some normal images with large areas of single color background, for example, images in ocean background. Another defect is that grey world algorithm may cause luminance variations in the channels having no cast. We note that though different in mean values, standard deviations of the three channels are supposed to converge in color cast images, which is not suitable for those false alarms. Based on this discrepancy, through a mathematical manipulation both on mean values and standard deviations of the three channels, a novel color correction model is proposed by weighting the gain coefficients in grey world model. All the three weighted gain coefficients in the proposed model tend to be 1 on images containing large single color regions so as to avoid false alarms. For the color cast images, the channel existing color cast is given a weighted gain coefficient much less than 1 to correct color cast, while the other two channels are distributed weighted gain coefficients approximately equal to 1 thus to ensure that the proposed model has little negative effects on channels with no color cast. Experiments show that our model presents better performance in color correction.