Using Fuzzy Inference and Cubic Curve to Detect and Compensate Backlight Image

This paper proposes a new algorithm method for detection and compensation of backlight images. The proposed technique attacks the weaknesses of conventional backlight image processing methods, such as over-saturation and diminished contrast. This proposed algorithm consists of two operational phases, the detection phase and the compensation phase. In the detection phase, we use the spatial position characteristic and the histogram of backlight images to obtain two image indices which can determine the backlight degree of an image. The fuzzy inference method is then used to integrate these two indices into a final backlight index which determines the final backlight degree of an image more precisely. The compensation phase is used to solve the over-saturation problem which usually exists in conventional image compensation methods. In this phase, we propose the adaptive cubic curve method to compensate and enhance the brightness of backlight images. The luminance of a backlight image is adjusted according to the cubic curve equation which adapts dynamically according to the backlight degree indicated by the backlight index estimated in the detection phase. The performance of the proposed technique was tested against 300 backlight images covering a variety of backlight conditions and degrees. A comparison of the results of previous experiments clearly shows the superiority of our proposed technique in solving over-saturation and backlight detection problems.