受煤矿井下粉尘、水雾和低照度环境影响，对皮带运输系统的监测图像精准识别极为困难。针对现有去尘雾方法的图像处理结果和效率欠佳的问题，提出一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法。首先利用阈值分割结合伽马变换修正通道差，解决因低照度环境影响导致的尘雾浓度较大区域与其他区域间像素值差异不明显的问题，修正后通过引导尘雾图像做引导滤波得到更加符合实际情况的全局大气光强；然后为解决暗通道先验在尘雾浓度较大区域失效问题，引入亮通道先验进行补充，使用通道分量来辅助暗通道及亮通道透射率融合，避免因多次分割而导致的边缘像素归属问题；最后将去雾后RGB图像转至HSV空间，对亮度分量进行直方图均衡化并将均衡化前后的亮度分量进行加权融合，采用客观指标评价，选择最优聚合权值进行聚合，同时考虑去雾过程中饱和度损失和亮度分量与饱和度分量间的相关性提出饱和度自适应矫正函数，对图像饱和度进行矫正，色调分量保持不变，随后将图像转回至RGB空间，得到亮度适中、信息保留丰富和色彩鲜艳的图像；为验证所提方法的有效性，采用主观视觉、客观指标和目标检测精度及置信度进行算法对比，实验结果表明所提方法在上述4个指标上均优于被对比算法，其图像细节保留丰富，图像视觉观感更佳。

Due to the influence of dust, water mist and low illumination environment in coal mine, it is very difficult to accurately identify the monitoring images of belt transportation system. Aiming at the problem of poor image processing results and efficiency of existing dust and fog removal methods, a dust and fog removal and enhancement method for low-illumination environment images based on dark-bright channel segmentation and fusion is proposed. Firstly, the channel difference is corrected by threshold segmentation combined with gamma transform to solve the problem that the difference of pixel values between the regions with large dust and fog concentration and other regions is not obvious due to the influence of low illumination environment. After correction, the global atmospheric light intensity which is more in line with the actual situation is obtained by guiding the original image to do guided filtering. Then, in order to solve the problem that the dark channel prior fails in the area with large dust concentration, the bright channel prior is introduced to supplement, and the channel component is used to assist the fusion of dark channel and bright channel transmittance, so as to avoid the problem of edge pixel attribution caused by multiple segmentation. Finally, the RGB image after dehazing is transferred to HSV space, the brightness component is histogram equalized and the brightness component before and after equalization is weighted and fused. The objective index evaluation is used to select the optimal aggregation weight for aggregation. At the same time, considering the saturation loss in the dehazing process and the correlation between the brightness component and the saturation component, the saturation adaptive correction function is proposed to correct the image saturation and keep the tone component unchanged. Then the image is transferred back to RGB space to obtain an image with moderate brightness, rich information retention and bright color. In order to verify the effectiveness of the proposed method, subjective vision, objective indicators, and target detection accuracy and confidence are used to compare the algorithms. The experimental results show that the proposed method is superior to the comparison algorithm in the above four indicators, and the image details are retained more abundant and the visual perception is better.