## 3 Experimental Results and Analysis
The average _MSE_, _PSNR_, _SSIM_, and _entropy_ are listed in Table 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **DCP** & **FDCP** & **GDCP** & **MDCP** & **FFA** & **HDCP** \\ \hline a & 253.79 & 251.86 & 254.90 & 211.89 & 118.87 & **102.52** \\ b & 255.00 & 254.94 & 255.00 & 202.09 & 167.91 & **147.05** \\ c & 254.92 & 254.74 & 254.98 & 204.36 & 195.47 & **155.55** \\ d & 254.50 & 254.54 & 229.28 & 196.64 & 196.83 & **195.81** \\ \hline \end{tabular}
\end{table} 
Table 1: MSE of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **DCP** & **FDCP** & **GDCP** & **MDCP** & **FFA** & **HDCP** \\ \hline a & 24.09 & 24.12 & 24.07 & 24.87 & 27.38 & **28.02** \\ b & 24.07 & 24.06 & 24.06 & 25.08 & 25.88 & **26.46** \\ c & 24.06 & 24.07 & 24.06 & 25.02 & 25.22 & **26.21** \\ d & 24.07 & 24.07 & 24.53 & 25.19 & 25.19 & **25.21** \\ \hline \end{tabular}
\end{table} 
Table 2: _PSNR_ of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **DCP** & **FDCP** & **GDCP** & **MDCP** & **FFA** & **HDCP** \\ \hline a & 6.6208 & 0.6534 & 0.4957 & 0.6719 & **0.7780** & 0.6795 \\ b & 0.4630 & 0.5876 & 0.3354 & 0.5447 & **0.6065** & 0.5466 \\ c & 0.5331 & 0.5813 & 0.5442 & 0.5344 & **0.6781** & 0.5881 \\ d & 0.5662 & **0.5710** & 0.4399 & 0.5075 & 0.4675 & 0.4737 \\ \hline \end{tabular}
\end{table} 
Table 3: _SSIM_ of different algorithms. Table 5 shows that the average _PSNR_s of all 6 methods are relatively close. The average _SSIM_ of HDCP is also in second place, and the gap between the first two algorithms is not huge. Compared with other DCP algorithms, the average _MSE_ of the proposed method decreases by 26.98%. The average _SSIM_ increases by 10.298%. The average _entropy_ increases by 7.780%. In order to show defogging abilities for different haze densities, the public dataset of NYU2 was then selected as the experimental object. The results are as follows:  
In Figure 5, the first row is the images with the continuously increased fog densities compared to the original image. The next rows correspond to the different restored images. In the results of FDCP, as the haze densities increase, the images show that the fog is not removed cleanly. The results of GDCP are unstable. The results of MDCP have a certain stability, except for the last image. FFA has the same results as FDCP. However, its objective evaluation indicators have deteriorated seriously. The proposed method in this paper can still completely remove the influence of fog. The processing results are basically stable even with the increase in haze densities. Figure 5: Each algorithm’s results for different haze densities comparison groups. The processing results are, respectively, from: (**I**): FDCP [26], (**II**): GDCP [27], (**III**): MDCP [15], (**IV**): FFA [19], and (**V**): HDCP. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average _MSE_** & **Average _PSNR_** & **Average _SSIM_** & **Average _Entropy_ \\ \hline DCP & 254.81 & 24.07 & 0.3508 & 7.1909 \\ FDCP & 252.95 & 24.10 & 0.4179 & 7.0646 \\ GDCP & 253.29 & 24.09 & **0.4920** & 7.2142 \\ MDCP & 187.34 & 25.41 & 0.4648 & 7.2110 \\ FFA & 178.79 & 25.61 & 0.4723 & 7.0621 \\ HDCP & **173.14** & **25.75** & 0.4758 & **7.7280** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the overall objective evaluation results of different algorithms on the enhancement of the O-HAZE dataset. For generality, all 1750 photos of the public dataset of NYU2 were processed. Among them, there were 250 individual scenes with 7 levels of haze densities. The evaluation criteria were calculated, and the statistics of the results are as follows. Table 6 shows that the proposed method in this paper still has a great advantage even with different haze densities. The performances of HDCP are all higher than the values of the compared algorithms. Among them, the average \(MSE\) decreases by 49.29%. The average information entropy increases by 3.029%.