# Dynamic Dark Channel Prior Dehazing with Polarization
## 4 Experimental Results and Discussion
### Polarization-Based Dark Channel Prior Processing  
During the experiment, multiple frames of foggy images were collected from each polarization detection channel. Figure 7a presents the original polarization images of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) at a certain time, subsequently referring to the horizontal direction. It can be observed that the quality of all polarization images is severely degraded due to the effect of scattering light component originating from suspended atmospheric particles, which is imaged as noise. Figure 7b further provides the corresponding polarization images processed by the dark channel prior dehazing method. By comparing Figure 7a with Figure 7b, the dark channel prior dehazing method can improve the image contrast by suppressing the impact of the scattering effect on the imaging process. In order to demonstrate the performance of polarization in improving target detection and recognition efficiency, a comparison imaging effect between the original, the dark channel prior, and the polarization-based dark channel prior images of the scene is conducted. In the original imaging condition, it is difficult to discriminate the objects from each other within the rectangular regions of 1, 2, and 3 in the image, as shown in Figure 8a. It could be attributed to the superposition of scattering light components on the fog-free image. In the dark channel prior to the imaging condition, the scene could be identified clearly due to the  
Figure 6: The fitting curve of the relationship between light intensity and the polarization axis. Figure 7: Different polarization channels for (**a**) original images and (**b**) dehazing images processed by the dark channel prior. removal of scattering light, as shown in Figure 8b. However, the traditional dark channel prior method uses intensity information as a carrier to describe the scene. As a result, it is hard to differentiate the target from its background with similar reflectivity, as presented in the designated rectangular regions. When the polarization is further used, the contrast between the target and the background in the designated rectangular regions is significantly improved, as shown in Figure 8c. This could be explained by the fact that polarization information is encoded by the polarization-based dark channel prior dehazing method using the degree of linear polarization. The obvious difference in polarization information between the target and the background allows them to be clearly identified from each other. This also provides a scientific basis to physically enhance the target features in foggy environments through the use of polarization information. In order to make a more objective evaluation of the effects of image processing, we take advantage of the non-reference image quality metric, the natural image quality evaluator (NIQE), to evaluate the results of image processing. According to the definition of NIQE, a lower NIQE indicates  
Figure 10: Cont. By observing Figure 9, it can be seen that the influence of scattering noise on the optical imaging process was effectively suppressed by the dark channel a priori defogging method compared with the original scene image, but the difference between the target and the background was not obvious due to its image representation based on intensity information. However, when polarization information was introduced into the dark channel a prior dehazing image, the target and the background could be clearly identified based on the larger difference in polarization information between them. The synchronous trigger circuit was adjusted to the closed state while the four polarization channels in the system started to synchronously acquire the polarization images of the same scene, corresponding to 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\).