基于无参考图像获取能见度的车载视频自适应去雾算法

In order to solve the problem of vehicle environment perception in foggy environment, aiming at the situation that the algorithm of vehicle visual distance defogging can not automatically acquire visibility, restored image color oversaturation, and loss of details seriously, etc, an improved non-linear dichotomy root-finding algorithm, which utilizes the spatial quality of no-BRISQUE revises the visibility in real time, and finally realizes the automatic evaluation of visibility, is proposed to overcome the non-real-time and subjectivity of visibility acquisition by traditional instrument measurement and visual measurement methods. An improved non-linear dichotomy root-finding algorithm is proposed,. The relationship function between atmospheric visibility and vehicle visual distance is improved. The error between the transmittance value calculated from the improved vehicle visual distance and the actual transmittance is reduced, the Halo effect is reduced, and the image details are increased. Experiments show that the transmittance estimation based on visibility can adaptively process haze video under atmospheric light scattering model. The reconstructed video image has clear picture quality, bright color and no distortion, and can retain a large amount of image information. The processing video is smooth and no jamming. It also has good fog removal effect for vehicle video with changing traffic scenes in foggy environment.