# Real-Time Vision through Haze Based on Polarization Imaging
## 5 Results and Discussion
### Testing Environment
The distance between building A and the detector was 1.6 km, and we can only observe the outline of the building under the influence of haze, which means the spatial resolution of Figure 7a at the detection distance of 1.6 km is 15 m. However, we can see two windows of building A in Figure 7b, which means the spatial resolution of Figure 7b at the detection distance of 1.6 km was 2 m. Furthermore, building B, which is completely invisible in Figure 7a, is highlighted in Figure 7b. These changes suggest that our imaging system can improve spatial resolution in severe weather conditions. However, the limitation of the proposed method is also obvious. There was a large amount of noise in the distant area. This was because when using Equation (1) to enhance the reflected light energy of distant targets, the noise existing there also tended to be amplified in line with the same trend [24]. Figure 8 shows the intensity distribution curve in the pixels of row 415 (counting from top to bottom) across the background and the target from Figure 6g-i, which provides an intuitive demonstration of the difference between the atmospheric light and the target light in the imaging scene, as well as changes in the contrast. When compared with the results obtained with the Schechner algorithm (red curve), the reconstruction result of our algorithm (blue curve) increases the atmospheric light-target light difference to a certain extent and enhances the contrast of the image. In Figure 8, the fluctuations of the blue curve are stronger than those of the red one, particularly at the target location. The fluctuation of the pixel intensity curve after reconstruction using the proposed algorithm increased significantly, which indicates that this algorithm is superior to that of the Schechner algorithm in improving the image contrast. Figure 9a-c shows the grayscale histograms corresponding to Figure 6g-i, respectively. This rendering of information provides a more intuitive representation of the characteristics of Figure 6g-i. When compared with the raw image that has its histogram concentrated in the right half of the panel, the reconstructed image exhibited a wider and more evenly distributed histogram. This result confirms that the proposed algorithm can effectively reduce the effects of scattering due to aerosols and various gases to increase image contrast and enrich image details. Figure 9d-f illustrates the pixel intensity distributions in the red, green and blue (RGB) channels of Figure 6g-i, respectively. We note that the pixel intensity distribution in Figure 9f was optimized over the distributions of the raw image and the image processed by means of the conventional polarization imaging algorithm. In addition, the proposed algorithm increased the dynamic range and stereovision of the image. Figure 7: **(a,b)** The zoomed-in view of the region of interest marked with a red rectangle in Figure 6g,i; A and B are two buildings with different distances from the detector in the scene. Figure 10 shows the results of the detection under different weather conditions. Figure 10a is the total intensity image, which was captured under rainy and foggy weather. Figure 10b is the detection result of Figure 10a using the proposed method, which proved the effectiveness of our method in this weather condition. Figure 10c shows the total intensity image of contaminated polluted lake water, which was captured under dense fog weather, where the pollution of the lake water was difficult to be observed because of the influence of haze, the overall contrast of the image was low, and the detection distance was limited. After the detection by our imaging system, as shown in Figure 10d, the influence of aerosols and various gases was removed. This system could directly observe the pollution of the lake, so that we could control the pollution of the lake. In addition, the detection distance of the imaging system was improved, which greatly reduces the cost. Figure 8: Horizontal line plot at vertical position pixel 415 in Figure 6g–i.