For the problems of weather restriction and insufficient samples due to the difficulty of database acquisition for the collection of traffic haze images in the process of digital twinning, a new atmospheric fogging model is proposed to expand the database of traffic haze images with different concentrations. Firstly, the atmospheric light value is calculated based on the principle of dark feature, and the estimation of atmospheric light is obtained by using the atmospheric light compensation method based on variance fluctuation. Secondly, the color attenuation prior is used to estimate the scene depth, and to solve the initial transmittance. Then, an image atmospheric fogging model is constructed, and the calculated atmospheric light estimation and atmospheric fogging transmission are substituted into the model, and the fogging density is adjusted by the haze coefficient. Finally, several traffic video fogging experiments are designed and evaluated. Experimental results show that with the increase of preset haze coefficient, the proposed algorithm can make the image tend to blur subjectively, and the objective index gradually change accordingly. The image quality degradation law is basically consistent with the real scene containing fog, which can be used to expand haze data set, with good effectiveness and practicability. By evaluating and comparing the fogging images with different defogging algorithms, it can be seen that the effect of the restored image is basically the same as the defogging effect of the actual image, further verifying the effectiveness of the fogging model.
