# Real-Time Vision through Haze Based on Polarization Imaging
## 5 Results and Discussion  
### Testing Environment  
All images in this study were acquired by use of a real-time optical sensing and detection system with four cameras aligned parallel to the optical axis. Figure 5 shows the photograph of the imaging system. The specifications of the cameras were as follows: 60\({}^{\circ}\) field of view, 8-mm focal length, and 4 million pixels. Different from other optical imaging systems, polarization imaging system only requires the installation of a polarizer in front of ordinary industrial cameras to achieve clear scene imaging. In this study, the type of camera is GS3-U3-41C6C-C, which is produced by Pointgrey based in vancouver, Canada, and the type of polarizer mounted in front of the camera isLPVISC100, this polarizer is produced by Thorlabs in the American state of New Jersey. MATLAB R2017b(developed by MathWorks in Massachusetts, America) was used to execute the real-time polarization image processing and enhancement algorithm, and the program was run on a computer with a Windows 7 (64-bit) operating system and an Intel Core i7-4790K processor with 32 GB RAM, this processor is produced by Dell based in Texas, America. The time required to process a frame of image on the system was 30 ms. The raw images obtained from the real-time optical sensing and detection system using four cameras parallel to the optical axis are shown in Figure 6. These images were acquired under hazy weather conditions, and the image quality was seriously degraded, and the presence of atmospheric light, which was produced via haze particles, seriously reduced the image contrast and the detection distance of the system. In our study, we first applied the conventional polarization image enhancement algorithm proposed by [PERSON] [20], and the result is shown in Figure 6h. The algorithms  
Figure 5: Real-time polarization dehazing imaging system. proposed by [PERSON] are the most classical of all polarization imaging algorithms [21; 22; 23], and their processing effect can represent the processing effect of most algorithms. When compared with the raw images, the images reconstructed by the conventional algorithm showed obvious visual enhancements as the atmospheric light at both the near and far ends of the images are removed, and the contrast was also improved. However, this enhancement algorithm resulted in overexposure in the sky region, thereby limiting the visual perception of the image. Figure 6i shows the reconstructed image obtained by applying the registration and enhancement algorithm proposed in this study. We first note that this algorithm effectively eliminates the effects of atmospheric light on the image and improved the contrast of the image. Further, the proposed algorithm reduced not only the effects of atmospheric light for objects at the near ends, but also for those at the far ends, thus creating a perception of stereovision. In addition, a comparison of Figure 6h,i demonstrates that the proposed algorithm not only avoided overexposure in the sky but also enriched the details of the image. Figure 6: (**a–d**) Polarization images corresponding to polarization angles of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\); (**e**) Polarization image with minimum airlight; (**f**) Polarization image with maximum airlight; (**g**) Total intensity image; (**h**) Detection result obtained using the method of Schechner; (**i**) Detection result obtained with proposed method; The zoomed-in view of the region of interest marked with a red rectangle in (**g**) and (**i**) will be shown in Figure 7a,b. The zoomed-in view of the region of interest marked with a red rectangle in Figure 6g,i is shown in Figure 7a,b.