# Dynamic Dark Channel Prior Dehazing with Polarization
## 4 Experimental Results and Discussion
### Polarization-Based Dark Channel Prior Processing
Next, the scene images at moments of \(t_{1}\)-\(t_{4}\) were selected as the research objects, as shown in Figure 10. As illustrated in Figure 10a, the dynamic target in a rectangular region of the original scene image is severely affected by a large number of suspended particles, making it difficult to distinguish the target itself and its trajectory. The contrast of the dynamic target in the dehazed scene is obviously improved, but its image expression based on its intensity information makes the difference between the target and the background not clear. As indicated in the degree of polarization images, the object hidden in the tree background on the construction tower crane can be easily identified by the degree of polarization in the image. This is because the degree of polarization image reflects the polarization difference between the target and the background, which enhances the contrast and edge information of the scene image. Based on these experimental results, the image processing effects of different schemes are quantitatively analyzed by calculating the contrast between the moving object on the tower crane and its background at moments \(t_{1}\)-\(t_{4}\). The corresponding calculation formula is  
\[ICR=|\left(\left.x_{i}-x_{0}\right)/\left(x_{i}+x_{0}\right)|, \tag{16}\]  
where \(x_{i}\) and \(x_{0}\) represent the average intensity of the moving target itself and the target background region. The specific contrast calculation results are shown in Table 2. At moments of \(t_{1}\)-\(t_{8}\), the polarization-based dark channel prior dehazing method increased the contrast of the dynamic target in the rectangular region 1-2 times, respectively, compared to the conventional method. The data in the table also indicate that the proposed method outperforms the traditional dark channel prior dehazing method when the contrast between different targets in the original scene is low, which further demonstrates that the proposed method can effectively overcome the limitations of the traditional scheme. Figure 10: Processing of dynamic scene images. **(a–e)** The comparison results of different scenes at moments \(t_{1}\)–\(t_{4}\). Results in Figure 8 are used again to further demonstrate the advantages of polarization information in the identification of targets, which are normalized by mapping it to the range of 0 to 1. The comparisons among direct imaging, dark channel prior and polarization-based dark channel prior, are shown in Figure 11a-c. The red dashed in Figure 11a-c represents the px position corresponding to scene image. The blue and green curves in Figure 11d correspond to the normalized light intensity distribution at the red dashed line position in Figures 11a and 11b, respectively, and the red curve corresponds to the degree of polarization distribution at the red dashed line position in Figure 11c. The curves of the scene images after dehazing by the dark channel prior are more jitter than those of direct imaging, which means the enhancement of image contrast. In addition to eliminating the influence of scattering noise on the optical imaging process, this method still makes it difficult to distinguish different objects with similar reflectance characteristics. The degree of polarization-encoded information fluctuates more intensely at different pixel positions than that of the intensity information, and the target information representation in the range of 400-600 px positions is more obvious. This result indicates that polarization contains rich information about objects, which can accurately highlight detail features and is conducive to target recognition. The above results demonstrate that the polarization-based dark channel prior dehazing method has the best characterization capability for the target. However, the research on the polarization information in the above scene images is based on the R, G, and B channels.