## 3 Experimental Results and Analysis
### Analysis of Experimental Results  
The general evaluation metrics for image enhancement include Average Local Standard Deviation (ALSD), Average Gradient (AG), Information Entropy (IE) and Contrast.  
In this paper, the above evaluation metrics are selected to evaluate images. The specific evaluation data are shown in Table 1 below, and the histogram of each enhancement algorithm after enhancement is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Metrics** & **Original Image** & **MSR** & **CLAHE** & **HF** & **PSO** & **Proposed Algorithm** \\ \hline ALSD & 25.85 & 31.24 & 44.24 & 55.94 & 38.52 & 73.47 \\ AG & 3.91 & 4.29 & 5.11 & 7.12 & 6.93 & 8.54 \\ IE & 6.27 & 6.72 & 6.69 & 7.15 & 6.79 & 7.23 \\ Contrast & 16,376.2 & 16,720.2 & 19,597.3 & 21,334.9 & 18,978.3 & 23,399.8 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of corridor image enhancement algorithms.  
Figure 9: Comparison of mine video image enhancement effect.  
Compared with the original image of the corridor, the enhanced images have been improved in terms of image brightness. However, the image after MSR enhancement appears over-enhanced, resulting in poor subjective image effects. The brightness of the image after CLAHE enhancement has been improved reasonably, but the visual effect is poor in terms of color retention. The image after HF enhancement has over-enhanced the contrast and neglected the detailed information, resulting in a lack of clarity in the detailed background. The algorithm based on common PSO has a good balance in all aspects, but the overall brightness and pattern details of the image still need to be improved compared with the proposed algorithm.  
As shown in Figure 11 above, compared with the original image, ALSD, AG, IE and contrast of the image enhanced by the proposed algorithm improve by 184.2%, 118.4%, 15.3% and 42.8%, respectively, which are better than other algorithms. In terms of the enhanced histogram, the proposed algorithm takes into account the distribution of different pixel values of the image while enhancing the brightness and retaining more image detail information.  
Figure 11: Percentage enhancement of corridor image.  
Figure 10: Enhancement histogram of corridor image.  
For the analysis of the actual coal mine image, the image of the conveyor belt is selected, and the specific evaluation data are shown in Table 2 below. The histogram after enhancement by each enhancement algorithm is shown in Figure 12.  
The actual coal mine image in Figure 9a has a darker background compared to the unevenly illuminated corridor image in Figure 8a. Subjectively, the images enhanced by each algorithm are enhanced in terms of image brightness. The brightness of the image enhanced by MSR is the most obvious, but there is over-enhancement in the belt brightness illumination region, which leads to the loss of belt edge contour information. The enhanced images after CLAHE and the algorithm based on common PSO show insignificant brightness enhancement in the regions with large brightness changes and still have noise interference. The image after HF enhancement focuses too much on the contrast between light and dark and loses the detailed information of the images.  
As shown in Figure 13, compared with the original image, ALSD, AG, IE and contrast of the image enhanced by the proposed algorithm improve by 50.9%, 31.8%, 30.2% and 11.6%, respectively.