Haze removal algorithm based on single-images with chromatic properties

Abstract Dense fog conditions formed by particles and water droplets in the atmosphere impact the road segmentation accuracy of Advanced Driver Assistance Systems (ADAS) by degrading the contrast and color fidelity of images. Existing road segmentation methods are not suitable for dense haze conditions; darkness, under-estimation, and over-enhancement problems occur after dehazing. This paper proposes a single-image haze removal technique based on hazy state detection and simple priors. Hazy state detection serves to identify the fog in an image by the linear relationship between brightness and saturation in HSV color space. Simple priors both in the background and hazy layers are based on a dichromatic reflection model (DRM) to accommodate different concentrations of fog. We find that according to color consistency and physical properties, fog and highlight appear to share the same chromatic properties. Based on this observation, the DRM can approximate the fog map. The maximum a posteriori formulation can also be utilized to robustly refine the fog map and remove black noise. We employ the fog map to compute atmospheric light and predict transmission rate in order to obtain haze-free images. Our results show that this method outperforms existing methods in regards to both efficiency and dehazing ability.