# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion
### Limitations of the Proposed Method  
In the evaluation results shown in Figure 15, although the proposed method demonstrates a clear advantage, there are instances where its performance is lower than or similar to the original DCP method. These cases typically involve images without sky regions or containing very dark elements. In images without sky regions, the physical significance of using low polarization values to determine atmospheric light is weakened. While our method can still probabilistically determine the atmospheric light region correctly in such images since in areas with heavier fog, their polarization is often lower, this is not a high-probability event. In images with very dark elements, the intensity across the four polarization angles becomes similar, resulting in lower polarization values in these areas. However, these darker regions actually imply lower fog concentration, and estimating them as atmospheric light regions would lead to poor dehazing results. Both scenarios introduce uncertainty into our method's polarization calculation, causing potential inaccuracies in estimating the atmospheric light region.  
Figure 17 shows an intentionally collected image that lacks a sky region and contains very dark elements. The green rectangle in Figure 17a represents the atmospheric light location estimated by the proposed method, while the red rectangle represents the atmospheric light location estimated by the original DCP method. For extremely dark regions, our polarization detector's energy values at the four angles are similar, resulting in a low calculated degree of polarization. Consequently, this region is selected as the atmospheric light estimation region by our approach. However, it is evident that this region is not a  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**Evaluation**} & \multirow{2}{*}{**Hazy**} & \multirow{2}{*}{**DCP**} & \multirow{2}{*}{**Tran’s**} & \multirow{2}{*}{**Fattal’s**} & \multirow{2}{*}{**RTVP**} & \multirow{2}{*}{**GLPF**} & \multirow{2}{*}{**Proposed**} \\  & & & & & & & & & \\ \hline \multirow{4}{*}{Scene 1} & Entropy & 7.2522 & 7.7512 & 7.8001 & 7.8011 & 7.8472 & 7.7801 & **7.9495** \\  & Contrast & 0.1918 & 0.2605 & 0.3704 & 0.3401 & **0.7401** & 0.2751 & 0.3486 \\  & SSIM & - & 0.8024 & 0.7725 & 0.8137 & 0.7714 & 0.8554 & **0.8987** \\ \hline \multirow{4}{*}{Scene 2} & Entropy & 6.8675 & 7.6453 & 7.8739 & 7.8429 & 7.8781 & 7.2325 & **7.8885** \\  & Contrast & 0.0391 & 0.0811 & **0.0947** & 0.0651 & 0.0798 & 0.0464 & 0.0791 \\  & SSIM & - & 0.6716 & 0.6357 & 0.7145 & 0.6681 & **0.8052** & 0.7768 \\ \hline \multirow{4}{*}{Scene 3} & Entropy & 7.8913 & 7.9469 & 7.8739 & 7.9478 & 7.9485 & 7.9256 & **7.9509** \\  & Contrast & 0.0853 & 0.1959 & **0.2654** & 0.2135 & 0.1978 & 0.1214 & 0.2515 \\ \cline{1-1}  & SSIM & - & 0.6161 & 0.6362 & 0.6478 & 0.6604 & **0.8151** & 0.7998 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluations of algorithms.  
reasonable atmospheric light estimation region. Ultimately, due to the underestimated atmospheric light estimation, the image-dehazing effect is weakened.