# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 4 Experiment and Analysis  
To assess the efficacy of the proposed method in this paper for low-light image enhancement in coal mines, MATLAB (Ver. 9.14) was used to conduct simulation experiments. Three low-light monitoring images of coal mines are selected and compared with SSR, MSR, and Ref. [6], whereby the first two are from publicly available images of underground coal mines on the Internet, and the latter is from an image taken from an actual underground coal mine surveillance video. The outcomes of the experiments are depicted in Figures 8-10. Figure 8: Image 1, enhanced by different algorithms. (**a**) Original image; (**b**) SSR algorithm; (**c**) MSR algorithm; (**d**) Ref. [6] algorithm; (**e**) [PERSON]’s algorithm. Figure 10: Image 3, enhanced by different algorithms. (**a**) Original image; (**b**) SSR algorithm; (**c**) MSR algorithm; (**d**) Ref. [6] algorithm; (**e**) [PERSON]’s algorithm. Figure 7: Histogram of initial and stretched S components. Figure 9: Image 2, enhanced by different algorithms. (**a**) Original image; (**b**) SSR algorithm; (**c**) MSR algorithm; (**d**) Ref. [6] algorithm; (**e**) [PERSON]’s algorithm. As can be seen from the above images, the original images are all images of coal mines in a low-light situation, and the images are darker overall, making it difficult to distinguish the details in them. The processing method using the SSR algorithm enhances the brightness of the images to some degree, but its brightness enhancement effect is limited, and the images still have distortion problems, resulting in a departure from the true color. On the other hand, for the processing method using the MSR algorithm, the brightness is greatly improved, and the details of the images are enriched. But the images are overall too bright, and halo artifacts are prone to appear in bright areas; moreover, the noise in the images are also amplified in the process, resulting in overall unclear and fuzzy images. The method used in Ref. [6] has richer details, but the color of the images shows some bias; the edges of the images are fuzzy, and the details are not clear. By contrast, the algorithm utilized in this study obviously improves the brightness of the images, reduces the halo blur phenomenon while improving the contrast, can better retain the edge details, and suppresses the influence of noise. Additionally, the color effect with our algorithm is more in line with human vision and can largely improve the quality of the coal mine images. To objectively assess the level of image enhancement, the effect of enhancement is evaluated through six metrics such as mean, standard deviation (SD), information entropy (IE), and average gradient (AG). The metrics have the following specific definitions:  
1. Mean \[\mu=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}I(i,j),\] (19) where \(MN\) refers to the dimensions of the image. Mean is employed to assess the average pixel value, which indicates the mean brightness; as the value increases, the image quality improves. 2. Standard deviation \[SD=\sqrt{\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}(I(i,j)-\mu)^{2}},\] (20) where \(\mu\) is the mean of the image. SD is employed to assess the spread of grayscale values among the image pixels, a larger value means a wider distribution of pixel values, that is, the greater the quality of the image. 3. Information entropy \[IE=-\sum_{i=1}^{M}p(i)\lg p(i)IE=-\sum_{i=1}^{M}p(i)\lg p(i)IE=-\sum_{i=1}^{M}p (i)\lg p(i),\] (21) where \(p(i)\) denotes the probability of a pixel appearing within the image. IE is utilized to assess the quantity of information presented within the image; a higher entropy value equals a more uniform distribution of pixel values in the image, and the image has higher levels of texture and detail. 4.