# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion
### Experimental Results
Overall, the proposed method demonstrates higher average SSIM and lower variance compared to the original DCP method. This suggests that, in most cases, the proposed method offers better image restoration performance and higher stability. In this experiment, we intentionally selected more images where the original DCP method inaccurately estimated the atmospheric light intensity to highlight the advantage of the proposed method in dehazing such images. Figure 14: Dehazing result using PolarLITIS dataset image. (**a**) Real image. (**b**) Foggy image. (**c**) Result using original DCP. (**d**) Result using the proposed approach. Figure 15: SSIM of original DCP and the proposed method. We evaluated the proposed method using the image sub region's contrast. Table 1 presents the contrast evaluation results for the regions marked with yellow rectangles in Figures 10, 13 and 14. The bold numbers in the table represent the maximum contrast values for each image under different algorithms. It can be observed that both the original DCP and the proposed method effectively enhance the contrast of the hazy images. However, the proposed method exhibits greater stability, with an overall higher enhancement compared to the original DCP, which results in a 28% improvement in SSIM and an increase in contrast of more than 20% when compared to the original DCP. We contrasted the proposed approach with two traditional dehazing approaches put forth by [PERSON] [25] and [PERSON] [26] as well as two more recent approaches, RTVP [15] and GLPF [12]. The dehazing outcomes of several algorithms under cloudy, polluted, and wet weathers are shown in Figure 16.  
In Figure 16, although DCP has a good capacity to restore the visual quality, it occasionally causes color changes or excessive darkness in the images and more visible halos around object edges. For instance, scene 1 has color changes, while all scenes have an overly dark picture. This problem is caused by the algorithm overestimating the atmospheric light as described in Section 3.1. Images dehazed using [PERSON]'s approach have stronger contrast, but the colors of the result are often over-saturated since his algorithm is not physically based and may underestimate the transmission. The statistical methodology used by [PERSON] requires enough color information and variance. If the haze is intense, his method cannot effectively estimate the transmission because the color is faint and the variance is too low, resulting in a dehazed image with localized darkening and jarring color transitions. RTVP uses scene polarization to compute the target energy prior to air transmission. This causes an overestimation of the target energy in picture regions with low polarization, resulting in overexposure in these areas. This effect is especially noticeable in the sky areas of Scenes 1 and 2. GLPF can improve the preservation of scene brightness, bringing it closer to the actual ambient light. However, its dehazing impact is weak, resulting in low visual contrast. Our proposed solution keeps the brightness of the image while providing a more prominent \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Hazy Image** & **DCP** & **Proposed** \\ \hline Figure 10a & 0.1918 & 0.2605 & **0.3486** \\ Figure 13a & 0.0853 & 0.1959 & **0.2515** \\ Figure 13b & 0.0391 & **0.0811** & 0.0791 \\ Figure 14b & 0.1683 & 0.3006 & **0.3154** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contrast comparison results. Figure 16: Comparison of results in hazy scenes from different algorithms. dehazing impact. Our approach exhibits good ambient light intensity and contrast in all scenes. It also does a good job at dehazing distant objects in hazy environments. As objective assessment measures for the dehazing methods, we employed image entropy, local contrast (calculated from the green rectangular region in Figure 16), and SSIM.