# Dark-Center Based Insulator Detection Method in Foggy Environment
## 4 Experimental Results and Analysis
### Defogging Experiments
Therefore, the five most representative image-defogging methods, namely dark channel prior, automatic color equalization, single-scale Retinex, DehazeNet network, and multi-scale Retinex, were selected for comparison tests, and the experimental results are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**t(x)** & **J** & **clahe** & **PSNR [dB]** & **SSIM** \\ \hline \hline \multirow{4}{*}{\(\blacktriangledown\)} & & & 15.80 & 0.78 \\  & & & 16.27 & 0.80 \\  & \(\blacktriangledown\) & & 16.16 & 0.78 \\  & & \(\blacktriangledown\) & 15.89 & 0.78 \\  & \(\blacktriangledown\) & & 16.35 & 0.79 \\ \hline \hline \end{tabular} Note: t(x) is the fitted transmittance estimate using the dual dark channel; b) is the optimization of the atmospheric light value estimate; clahe is the CLAHE with repair module. \(\blacktriangledown\) means with.  
\end{table} 
Table 1: Improved dark channel prior defogging algorithm for ablation experiments. As seen in Figure 10, the dark-channel-based prior method (DCP) can make the image clearer in visual effect after processing, but the image becomes darker overall, as shown in Figure 10c; the automatic-color-balance-based defogging algorithm (ACE) enhances the contrast of the image but produces more noise, which has a greater impact on the subsequent image recognition work, as shown obviously in Figure 10d; while the single-Retinex-model-based scale and multi-scale defogging algorithms (SSR) for foggy images are less effective, as shown in Figure 10e,f; deep-learning-based DehazeNet defogging algorithm does not obtain good results demonstrated for this scene, as shown in Figure 10g; the improved defogging algorithm (Dark) performs better compared with the previous five algorithms, and the image quality has been significantly improved. The image saturation and color are enhanced, too. Table 2 shows the performances of the six defogging algorithms on the CPILD test set. As seen in Table 2, the proposed defogging algorithm has the best PSNR and SSIM metrics, with PSNR values of 0.55, 0.24, 4.73, 3.82, and 0.12 dB higher relative to the dark channel prior (DCP), automatic color equalization (ACE), single-scale Retinex (SSR), multi-scale Retinex (MSR), and DehazeNet, respectively. As for SSIM values, the proposed algorithm is 0.8, among which is the closest to 1. The best PSNR and SSIM values indicate that the proposed algorithm can process foggy images with the least image distortion, best defogging effect, and highest image quality. For the comparison of information entropy, the SSR algorithm has the lowest recovered information in the test set, which is only 6.41 bits. ACE obtains the next best information entropy result, with a value of 7.23 bits. The proposed algorithm has the best-recovered result, with an information entropy of 7.55 bits, which indicates that the proposed algorithm has a better image-defogging effect than other algorithms, the best image quality, rich information carried, and less image distortion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **PSNR [dB]** & **SSIM** & **S [bit]** \\ \hline DCP & 15.80 & 0.78 & 7.04 \\ ACE & 16.11 & 0.79 & 7.23 \\ SSR & 11.62 & 0.76 & 6.41 \\ MSR & 12.53 & 0.77 & 6.54 \\ DehazeNet & 16.23 & 0.80 & 7.34 \\ Dark & 16.35 & 0.80 & 7.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of 6 different defogging algorithms. Figure 10: Comparison of the results of different defogging algorithms.