Density Estimation of Fog in Image Based on Dark Channel Prior - Hong Guo; Xiaochun Wang; Hongjun Li (Atmosphere, 2022)
This paper proposes a method and an original index for the estimation of fog density using images or videos. The proposed method had the advantages of convenient operation and low costs for applications in automatic driving and environmental monitoring. The index was constructed based on a dark channel map and the pseudo-edge details of the foggy image. The effectiveness of the fog density index was demonstrated and validated through experiments on the two existing open datasets. The experimental results showed that the presented index could correctly estimate the fog density of images: (1) the estimated fog density value was consistent with the corresponding label in the Color Hazy Image Database (CHIC) in terms of rank order; (2) the estimated fog density level was consistent with the corresponding label in the Cityscapes database and the accuracy reached as high as 0.9812; (3) the proposed index could be used to evaluate the performance of a video defogging algorithm in terms of residual fog.