# Real-Time Vision through Haze Based on Polarization Imaging
## 6 Conclusions  
In this study, we proposed a real-time polarization imaging algorithm and investigated its performance, both theoretically and experimentally. We designed and constructed a real-time optical sensing and detection system based on the Stokes vectors underpinned by the principles of polarization imaging, wherein optical analyzers at different angles relative to the polarization direction of the incident light were integrated into four independent cameras individually. Linear polarization components were calculated by use of the Stokes vectors, followed by real-time image registration of images with different polarization components based on the SURF algorithm and subsequent visualization of the polarization images. Finally, we adopted an improved image enhancement algorithm using the CLAHE-based bilinear interpolation to generate real-time high-contrast and high-definition images. Our experimental results further reinforce the conclusion that the proposed method can acquire high-contrast and high-definition images in real time without loss of spatial resolution, which improves the detection range of environmental monitoring stations in hazy weather conditions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Image} & \multirow{2}{*}{Metrics} & \multirow{2}{*}{**Mean Gradient**} & \multirow{2}{*}{**Edge Strength**} & \multirow{2}{*}{**Contrast**} \\ \cline{3-4}  & & & & \\ \hline \multirow{2}{*}{Figure 6g} & Total light intensity image & 0.0049 & 0.0523 & 1.3455 \\  & Result by the conventional algorithm & 0.0084 & 0.0897 & 5.0434 \\  & Result by the proposed algorithm & 0.0233 & 0.2443 & 17.5323 \\ \hline \multirow{2}{*}{Figure 8a} & Total light intensity image & 0.0066 & 0.07 & 1.9452 \\  & Result by the conventional algorithm & 0.0095 & 0.101 & 5.3448 \\  & Result by the proposed algorithm & 0.0245 & 0.2575 & 22.656 \\ \hline \multirow{2}{*}{Figure 8d} & Total light intensity image & 0.0048 & 0.0505 & 0.9295 \\  & Result by the conventional algorithm & 0.0055 & 0.0589 & 1.6168 \\ \cline{1-1}  & Result by the proposed algorithm & 0.0155 & 0.165 & 9.1692 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective evaluation of dehazed images.  
Figure 11: (**a,d**) Total intensity images of different scenes; (**b,e**) Detection results obtained by the method of Schechner; and (**c,f**) Detection results obtained with the proposed method.  
**Author Contributions:** This research article contains five authors, including [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. [PERSON] and [PERSON] jointly designed the overall architecture and related algorithms. [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] coordinated the overall plan and direction of the experiments and related skills. [PERSON] wrote this paper.  
**Funding:** This research was funded by Jilin Province Science and Technology Development Plan Project (approval number: 20160209006X, 20170309001 GX); Self-fund of State Key Laboratory of Applied Optics (approval number: 2016Y6133 FQ164); and Open-fund of State Key Laboratory of Applied Optics (approval number: CS16017050001).  
**Acknowledgments:** The authors would like to thank [PERSON] from XiDian university for her useful writing advises. The authors would also like to thank [PERSON], [PERSON] and [PERSON] for the help about the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.