Existing methods for detecting atmospheric visibility are easily influenced by subjective factors and equipment complexity. To address this issue, this study proposes a new algorithm for estimating atmospheric visibility based on image processing. First, combined with the dark channel prior theory, a method for estimating global atmospheric light values, based on the difference between image brightness and saturation, is introduced to obtain the atmospheric transmittance. Next, curvature filtering is used to refine the transmittance. Then, atmospheric visibility is estimated through the lane line detection technology and the extinction coefficient. Finally, a visibility correction model based on a linear regression equation is established to correct the estimated atmospheric visibility. Experimental results show that the proposed algorithm is accurate and practical for visibility estimation in traffic monitoring scenes in foggy weather.