# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 5 Results and Discussion
### Experimental Results
As a result, the restored red color is hardly visible, and the contrast between the windows and the structure is minimal, making it difficult to discern their borders. Because of the improved color restoration effect, the contrast between the building and the windows is larger in Figure 11b, and their borders are clearly discernible. Our approach keeps a better blue component in the sky region, resulting in a more natural and realistic sky background. In comparison, the original DCP approach produces a darker sky area with a slightly buried blue component. We use the PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index) [24] to objectively evaluate the quality of the dehazed images. The PSNR of Figure 10b is 13.0365, with an SSIM of 0.8024, while Figure 10c has a PSNR of 16.2242 and an SSIM of 0.8987. A higher PSNR indicates better image quality after processing compared to the original image, while a larger SSIM indicates greater structural similarity between the processed and original images. It can be observed that our method, when compared to the original DCP algorithm, preserves the structural integrity of the original image better and achieves higher image quality. To provide a clearer and more intuitive demonstration of the effectiveness of our proposed method, Figure 12 showcases the results in a concatenated manner. At the boundary between the building and the sky region, there is a certain amount of halo and stratification (particularly prominent in the red rectangle region), which overall creates an unpleasant visual experience. Figure 10: Results obtained using dehazing approaches. (**a**) Foggy image. (**b**) Result obtained using original dark channel prior approach. (**c**) Result obtained using the proposed approach. (**d**) Dark channel map of foggy image. (**e**) Transmission map of original dark channel prior approach. (**f**) Transmission map of the proposed approach. In fact, our method remains effective in handling images taken during rainy and smoggy weather conditions. Figure 13 demonstrates the dehazing effect on images captured  
Figure 11: (**a,b**) The zoomed-in view of the region of interest marked with green rectangles in Figure 10b,c. Figure 12: Dehazing result. **left:** Proposed approach. **middle:** Foggy image. **right:** Original dark channel prior approach. under rainy and smoggy weather. The proposed method exhibits favorable performance in terms of clarity and color restoration. To objectively evaluate the effectiveness of our method, we selected haze-free images from the publicly available dataset PolarLITIS. We then applied a fogging process to obtain the foggy versions of these images. Subsequently, we used both the original dark channel prior (DCP) method and our proposed method to perform haze removal on these foggy images. Finally, we utilized the SSIM to quantify the similarity between the dehazed images and the haze-free images in the dataset. A higher SSIM value indicates that the dehazed image is closer to the original haze-free image, which also signifies better dehazing results. Figure 14 presents the experimental results. Subjectively, compared to the original DCP method, the proposed method shows greater similarity to the real haze-free image in terms of color and brightness. Objectively, the SSIM value between the original DCP result and the original haze-free image is 0.7235, while the SSIM value between the proposed method's result and the original haze-free image is 0.9276. This indicates that the proposed method better restores the real scene. Figure 13: Dehazing effect on rainy weather and smoggy weather image. (**a**) The dehazing effect on images captured during rainy weather. (**b**) The dehazing effect on images captured during smoggy weather. We applied both the original DCP method and the proposed method to 95 artificially hazy natural scene images. We evaluated the SSIM between their respective dehazed results and the real haze-free scene images. Figure 15 presents the results of the SSIM evaluation.