## 4 Experimental Verification  
In order to verify the effectiveness of the improved Retinex algorithm (BF-MSRCR) for underground coal mine surveillance image enhancement, simulation experiments were carried out using Matlab programming for the same data set in the AMD R7, 16 G hardware environment, Windows 11 operating system. The images used in this paper are partly from publicly available underground coal mine images on the internet and partly from a real underground coal mine surveillance video. We extracted the video in frames, generated hundreds of coal mine surveillance images, and enhanced some of them. We selected a coal mine underground video and some underground images for training when testing the algorithm, and tested about 600 images in total.The specific images of the experiments are shown in Figure 7, using the MSR algorithm and MSRCR algorithm for comparison experiments. To make the image contrast enhancement effect more intuitive to represent, the method of grey level distribution is used in the verification, using the grey level histogram to verify that when the image contrast is high, its grey level distribution has a uniform and wide distribution. The validation process uses the MSR algorithm, MSRCR algorithm and BF-MSRCR algorithm for comparison and validation of the image enhancement effect based on the generated greyscale histogram, and the validation results are shown in Figures 8-10. Figure 6: Structure diagram for processing the luminance component V.  
Figure 8: Image 1 algorithm and greyscale histogram comparison. (**a**) Image 1 algorithm comparison; (**b**) Image 1 algorithm and greyscale histogram comparison. Figure 7: Images used in the experiment. (**a**) downhole image 1; (**b**) downhole image 2; (**c**) downhole image 3. Figure 9: Image 2 algorithm and greyscale histogram comparison. (**a**) Image 2 algorithm comparison; (**b**) Image 2 algorithm and greyscale histogram comparison. To show more experimental results, we selected some more frame-separated intercepted images from the coal mine video for enhancement to verify the effectiveness of the algorithm. The experimental results are shown in Figure 11.  
Figure 11: Comparison of video interception image algorithms. (**a**) Video interception image 1 algorithm comparison; (**b**) video interception image 2 algorithm comparison; (**c**) video interception image 3 algorithm comparison; (**d**) video interception image 4 algorithm comparison. Figure 10: Image 3 algorithm and greyscale histogram comparison. (**a**) Image 3 algorithm comparison; (**b**) Image 3 algorithm and greyscale histogram comparison. According to the visual verification of the greyscale histogram, the MSR algorithm enhances the image, the blurred edges and significant light points are more apparent, and the MSRCR algorithm shows some improvement. However, the colour distortion of the image is still more severe, and the histogram distribution of the above two algorithms is more concentrated. However, the BF-MSRCR algorithm proposed in this paper has a specific improvement in the image's edge sharpness and colour saturation, and the grey level distribution is wide and uniform. Therefore, the algorithm in this study has better contrast enhancement than the MSR algorithm and the MSRCR algorithm. A series of existing image quality evaluation criteria were subsequently chosen to measure the image enhancement effect of different algorithms. There are three general types of objective evaluation of image quality: the complete reference, partial reference and no reference. In this study, the peak signal-to-noise ratio (PSNR) [28], structural similarity (SSIM) [29], standard deviation, mean gradient, mean value, and colour image information entropy [30; 31] were used to measure the quality of the image, thus providing a more intuitive perception of the enhancement effect of the algorithm.