Fog-degraded image enhancement based on single-scale Retinex

Image enhancement is important for intelligence management system. It has potential applications in many fields such as traffic management，highway toll collection station，ship, airport and etc. The adjustment ability of logarithmic function, hyperbolic tangent function and inverse hyperbolic tangent function for fog-degraded images were compared. It was proved that hyperbolic tangent function had better ability to regulate brightness than logarithmic function. Accordingly, an image enhancement algorithm based on single scale Retinex was proposed. The algorithm conversed RGB color space to HSV space. Hue was kept unchanged. The hyperbolic tangent function based on center self-adaptive adjustment was used to enhance overall brightness of image. Local non-linear transformation was adopted to improve local contrast of images. And linear stretching was used to adjust saturation. Then color compensations are achieved. Experiments show that this kind of algorithm has significant effects on defogging and making color full of nature. Combined with parameters such as variance, entropy and arithmetic, experiments were conducted to make comparisons for algorithms based on single scale Retinex and multi-scale Retinex. It is proved that algorithm based on single scale Retinex has more advantages on image contrast and detail enhancement. And it has faster arithmetic speed. Generally, the algorithm based on single scale Retinex can be applied in real-time image processing.