# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion
### Experimental Results  
Figure 9 represents the intensity images of four polarization angles and polarized degree images acquired using the polarization camera. From Figure 9f, it can be observed that the polarization degree is quite low in the sky region. Among them, the portion highlighted by the green rectangle exhibits the lowest polarization degree. According to the proposed method, the intensity value of this region in the original image is used as an estimate for the atmospheric light. Figure 10 presents the actual captured and dehazed results using our system. It can be observed that when the haze is relatively bright, the classical dark channel prior dehazing method (Figure 10b) exhibits estimation bias in atmospheric light \(A\). It considers the brightness of the red rectangle region in Figure 10a as the estimate for atmospheric light. This region corresponds to a white traffic sign on the ground, and its value in dark channel map shown in Figure 10d is higher than that of the sky region. In reality, this region has the highest brightness values in both the dark channel image and the original image. In Figure 9f, the red rectangle region represents the location of the highest pixel value in the dark channel map of the foggy image shown in Figure 10a. However, in the polarimetric image, the corresponding pixel has a high polarimetric value. By using our atmospheric light estimation method, the intensity value at this position would not be selected as an option for the atmospheric light value. On the other hand, the green rectangle region has the lowest polarimetric value, and its intensity value in the original foggy image Figure 10a is chosen as our final estimate for the atmospheric light intensity. In Figure 10a, the average dark channel value within the red rectangle region is 253, which is close to saturation, while the dark channel value within the region marked with a red rectangle is 132. It is evident that the estimation method for atmospheric light in the original dark channel prior algorithm has overestimated the intensity value of the atmospheric light. In Section 3.1, we conduct an analysis of the influence of the atmospheric light intensity on the dehazing algorithm. It is observed that an overestimation of the atmospheric light intensity can lead to a darkening of the overall image, localized darkening regions, increased noise, prominent layering, and overall distortion in the image. Figure 10b shows the result of the original dark channel prior to the dehazing method. It can be observed that compared to the original image, this image has a significant reduction in brightness, overall lower brightness, and substantial color distortion in the blue sky region. Figure 10c displays the result after using the proposed approach. It preserves the original image's brightness while ensuring effective dehazing. The color restoration of the sky region is notably improved. Figure 10e,f illustrate the dark channel map and transmission maps estimated by the original dark channel prior method and the proposed method. As indicated by Equation (7), due to the overestimation of atmospheric light by the DCP method, the estimated transmission rate of the image becomes higher. Moreover, as stated in Equation (8), higher transmission rates result in overall darkening and color shifts in the dehazed image. By em  
Figure 9: Polarization images. (**a–d**) Intensity images of four polarization angles obtained after polarization image decomposition. (**e**) Intensity image composed from (**a–d**) using Equation (9). (**f**) DOP image. ploying the proposed method for re-estimating atmospheric light, the transmission rate of the image is reduced, leading to a more natural brightness and color in the dehazed image. The green rectangular sections in Figure 10b,c are expanded in Figure 11a,b. Due to the original DCP algorithm's overestimation of the ambient light intensity, the color of the building looks darker in Figure 11a, and the intensity of the red component in RGB diminishes.