# High-Brightness Image Enhancement Algorithm
## 4 Comparative Analysis
### Objective Analysis  
We calculated four objective indicators, including the structural similarity (SSIM), peak signal-to-noise ratio (PSNR), image information entropy (H) and executive time. The bigger the first three indicators, the better. The specific values are shown in Tables 1-5 below. Table 1 shows the objective values of Figure 4. Table 2 shows the objective values of Figure 5.  
Figure 7: Experimental results: (**a**) original image, (**b**) [PERSON] [35], (**c**) [PERSON] [36], (**d**) [PERSON] [21], (**e**) [PERSON] [37], (**f**) proposed.  
Figure 5. Table 3 shows the objective values of Figure 6. Table 4 shows the objective values of Figure 7.  
As shown in the tables, we can see that our algorithm ranked first, especially in terms of the SSIM and PSNR. Our algorithm was also better than that of [PERSON] [37] in terms of the SSIM. SSIM is an index used to measure the similarity of two images. We got images with different illumination intensity at different times from the video, including lossless images and distorted images. We processed the high-brightness images through the proposed algorithm. Comparing the resulting image with the lossless, we evaluated the quality of image restoration. SSIM reflects the comprehensive similarity of two images in brightness, contrast and structure. By averaging the objective indicators of the 50 high-brightness video images, the average PSNR of our algorithm was 29.53, which was 7.22 higher than that of the algorithm by [PERSON] [37]. In terms of the PSNR, the proposed algorithm was better than the algorithm of [PERSON] [37]. This shows that after compression, the image quality was better than that obtained by the algorithm of [PERSON] [37]. The larger the PSNR value, the smaller the image distortion. \"H\" reflects the average amount of information in the image and demonstrates the richness of image information from the perspective of information theory. Generally, the bigger the H value, the better. Our method ranked second in H. For the time part, because the generation of a guidance map takes a long time, our algorithm was not dominant in all comparative experiments. This will be our next optimization direction.  
In summary, by combining a subjective evaluation and an objective analysis, we can conclude that the proposed algorithm could effectively recover the exposure area information from high-brightness video images with sound visual effects, and the objective indicators were much better than those produced by the compared algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **H** & **SSIM** & **PSNR** & **Time(s)** \\ \hline [PERSON] [35] & 7.08 & 0.38 & 10.91 & 123.5 \\ [PERSON] [36] & 7.96 & 0.65 & 16.12 & 10.6 \\ [PERSON] [21] & 7.61 & 0.56 & 14.36 & 50.2 \\ [PERSON] [37] & 6.95 & 0.87 & 22.24 & 44.3 \\ Proposed & 7.66 & 0.95 & 30.05 & 225.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective values of the images shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **H** & **SSIM** & **PSNR** & **Time(s)** \\ \hline [PERSON] [35] & 7.25 & 0.51 & 12.68 & 125.7 \\ [PERSON] [36] & 7.92 & 0.65 & 15.58 & 10.7 \\ [PERSON] [21] & 7.31 & 0.60 & 13.86 & 53.7 \\ [PERSON] [37] & 6.84 & 0.89 & 24.32 & 45.2 \\ Proposed & 7.48 & 0.94 & 28.86 & 223.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Objective values of the images shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **H** & **SSIM** & **PSNR** & **Time(s)** \\ \hline [PERSON] [35] & 7.34 & 0.65 & 14.14 & 125.8 \\ [PERSON] [36] & 7.90 & 0.72 & 17.18 & 10.6 \\ [PERSON] [21] & 7.41 & 0.67 & 14.69 & 50.6 \\ [PERSON] [37] & 6.69 & 0.82 & 19.64 & 43.1 \\ Proposed & 7.51 & 0.95 & 29.65 & 221.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective values of the images shown in Figure 6.