Image backlight compensation using neuro-fuzzy networks with immune particle swarm optimization

In this study, we proposed a new technique to compensate the backlight images. Two processing stages, called the backlight level detection and the backlight image compensation, are proposed. In the backlight level detection stage, we first transferred the color space to gray space by feature weighting, then obtain two backlight factors. We apply these two backlight factors to the proposed functional-link-based neuro-fuzzy network (FNFN) with immune particle swarm optimization (IPSO) for detecting compensation degree. In the backlight image compensation stage, we also proposed the adaptive cubic curve method to compensate and enhance the brightness of backlight images according to the compensation degree of each image. The backlight degree is indicated by histograms of the luminance distribution in the backlight level detection stage. The experiment results showed that the backlight images can be compensated effectively.