# Nighttime Image Dehazing Based on Point Light Sources
## 5 Conclusions  
In this paper, a nighttime dehazing algorithm based on the light source influence matrix is proposed for the foggy environment at night. Different from the DCP estimation method of constant atmospheric light values, our light source influence matrix is more suitable for night scenes. Furthermore, the processed image by our method does not occur overexposed near the light source. To enhance the detail of image, an edge filter is introduced to fuse the transmittance of the near and far light source areas, and the correction with MRP makes the image more colorful. In addition, we introduce bias  
Figure 7: Results of dehazing at different concentrations. (\(\alpha\) means the degree of fog concentration) (1) Origin image. (2) DCP [14]. (3) NDIM [25]. (4) OSFD [35]. (5) OURS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SSIM (\(\dagger\))** & **PSNR (\(\dagger\))** & **NIQE (\(\ddagger\))** \\ \hline  & \multicolumn{3}{c}{\(\alpha=0.3\)} \\ \hline DCP & 0.8076 & 18.3604 & 12.9122 \\ NDIM & 0.6148 & 17.0594 & 10.9517 \\ OSFD & 0.7026 & 18.6044 & 12.9762 \\ OURS & 0.7796 & 20.2292 & 10.5981 \\ \hline  & \multicolumn{3}{c}{\(\alpha=0.5\)} \\ \hline DCP & 0.8572 & 17.3624 & 13.7685 \\ NDIM & 0.5479 & 15.9944 & 12.4291 \\ OSFD & 0.6508 & 16.7321 & 12.5742 \\ OURS & 0.8044 & 22.0709 & 11.4440 \\ \hline  & \multicolumn{3}{c}{\(\alpha=0.7\)} \\ \hline DCP & 0.9098 & 18.0504 & 14.3905 \\ NDIM & 0.4793 & 14.6109 & 14.3162 \\ OSFD & 0.6572 & 16.8288 & 13.8864 \\ OURS & 0.8575 & 25.3665 & 12.4637 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of algorithms at different fog concentrations.  
coefficients to make the algorithm effective in dense fog situations. Therefore, our method is better adapted to night scenes. The performance of the algorithm is quantitatively analyzed from three aspects: PSNR, SSIM, and UQI. The experimental results show that the proposed algorithm is effective in removing fog at night. In terms of PSNR, SSIM, and UQI, this method improves 34%, 22%, and 21% over the existed night-time defogging method OSPF. However, the time complexity of the algorithm increases as the size of the image increases. Refining the dark part and maintaining the time complexity is the main direction of future research. With the development of artificial intelligence, 5G communication, and other technologies, the next step will be to improve the dehazing algorithm to achieve video stream dehazing, or integrate the dehazing algorithm improvement into the target detection field.  
Conceptualization, X.-W.Y.; methodology, X.-W.Y. and X.Z. ([PERSON]); validation, X.-W.Y., X.Z. ([PERSON]) and Y.Z.; formal analysis, X.-W.Y.; investigation, X.Z. ([PERSON]); resources, X.Z. ([PERSON]) and W.X.; data curation, X.Z. ([PERSON]); writing--original draft preparation, X.Z. ([PERSON]) and X.-W.Y.; writing--review and editing, Y.Z.; visualization, X.Z. ([PERSON]) and Y.Z.; supervision, W.X.; All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Natural Science Foundation of China (Grant No. 61772471);The Project of Unified Application Engine for Smart Community Platform (Grant No.KYYHX- 20200336).  
Not applicable.  
The authors declare no conflict of interest.