# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 1 Introduction  
Video monitoring is one of the several important means of coal mine safety management; however, the quality of the video monitoring images is poor due to the influence of light, dust, and water mist. Traditional algorithms for enhancement are liable to halo blurring, unclear edge details, and noise pollution, which reduces the effectiveness of the information contained in the image [1], thereby affecting the performance of the video monitoring system. Therefore, improving the visibility of mine images in low-light conditions is essential, which is also a prerequisite for improving the performance of the subsequent analysis and recognition of video images. In recent years, many methods have been suggested by researchers to address the problem of enhancing mine images, which can be roughly divided into two types, those in the spatial domain and those in the frequency domain. Among them, the introduction of the Retinex algorithm has significantly enhanced the performance of image enhancement in coal mines, and researchers have conducted many studies based on this algorithm. Most of the coal mine image enhancement algorithms are rooted in the theory of Retinex, predominantly include single-scale Retinex [2], multi-scale Retinex [3], and multi-scale Retinex with color restoration (MSRCR) [4]. The Retinex algorithm can improve image contrast and enrich detail in images [5], but it results in excessive enhancement and color imbalances. [PERSON] et al. [6] introduced a method based on a nonlinear function that is based on the Retinex algorithm. Using a Gaussian filter to obtain the illumination componentand constructing a nonlinear function for contrast enhancement, the enhanced image and the initial image are integrated to improve the contrast and reduce the impact of uneven lighting. [PERSON] et al. [PERSON] et al. (2018) used a linear function to stretch the grayscale range of the brightness component, which was based on the Retinex algorithm model, and used a mapping function to perform color correction on the saturation component, avoiding the problem of low image contrast and achieving global enhancement. [PERSON] et al. [PERSON] et al. (2018) proposed a method that combines Gabor filter with the principles of Retinex theory, which isolates the intensity component from the HSI space and enhances it with MSRCR, and then the SSR algorithm is applied using Gabor filter on the image in the RGB space. The weighted fusion of the images is consequently enhanced by the two different methods, which avoids an over-enhancement of the image. [PERSON] et al. [PERSON] et al. (2018) proposed a Retinex framework that utilizes bilateral filtering to extract the reflection component in the HSI space, then transfers the image back to the RGB space to enhance it by combining a modified framework model with Gaussian pyramid transform, which reduces the blurring phenomenon and improves the quality of the image. [PERSON] et al. [PERSON] et al. (2018) presented an adaptive image enhancement technique that utilizes the Retinex algorithm along with guided filtering to process the V component in the HSV space; it performs color compensation for saturation component, histogram equalization on the initial image to improve the contrast, and a fusion processing of the two, which is richer in information and increases the details. The above analysis shows that, although the current algorithms have the capability to improve the image brightness, they still have problems, such as the blurring of local halos and difficulty in retaining edge details. To address the above problems, this paper proposes an improved image enhancement algorithm. The algorithm processes the different components separately in HSV color space, and finally converts them back to the RGB space.