矿井井下视频采集过程中由于照明系统分布不均匀、环境中存在大量粉尘和雾气，导致监控画面图像存在局部光线过曝、局部亮度不足、对比度低和边缘信息弱等问题。针对上述问题，提出了一种矿井井下非均匀照度图像增强算法。该算法基于Retinex−Net网络结构改进，具体包括非均匀光照抑制模块（NLSM）、光照分解模块（LDM）和图像增强模块（IEM）3个部分：NLSM对图像中人工光源局部非均匀光照进行抑制；LDM将图像分解为光照层和反射层；IEM对图像光照层增强，经伽马校正，最终得到增强图像。在NLSM和LDM中均采用Resnet作为网络基础架构，并顺序引入了卷积注意力机制中通道注意力模块和空间注意力模块，以增强对图像光照特征关注度和特征选择的效率。实验结果表明：① 选取MBLLEN，RUAS，zeroDCE，zeroDCE++，Retinex−Net，KinD++及非均匀照度图像增强算法对多种场景（井下运输环境场景、单光源巷道场景、多光源巷道场景、矿石场景）图像进行增强处理及定性分析，分析结果指出非均匀照度图像增强算法能够避免人工光源区域的过度增强，未在光源区域产生晕染和模糊现象，不易产生色偏，对比度适中，画面视觉效果更真实。② 选取信息熵（IE）、平均梯度（AG）、标准差（SD）、自然图像质量评价指标 （NIQE）、结构相似性（SSIM）和峰值信噪比（PSNR）作为评价指标，定量比较图像增强画面质量。结果表明非均匀照度图像增强算法在多种场景下处于相对领先地位。③ 消融实验结果表明，非均匀照度图像增强算法在NIQE，SSIM，PSNR这3个评价指标上均获得了最优结果。

Due to the non-uniform distribution of lighting systems and the presence of a large amount of dust and mist in the environment during the underground video collection process, there are problems with local light overexposure, insufficient brightness, low contrast, and weak edge information in the monitoring image. In order to solve the above problems, an image enhancement algorithm for non-uniform illumination in underground mines is proposed. This algorithm is based on the improvement of Retinex-Net network structure, which includes three parts: non-uniform illumination suppression module (NLSM), illumination decomposition module (LDM), and image enhancement module (IEM). Among them, NLSM suppresses local non-uniform illumination of artificial light sources in the image. LDM decomposes the image into light and reflection layers. IEM enhances the illumination layer of the image, undergoes gamma correction, and ultimately obtains the enhanced image. Resnet is adopted as the infrastructure of the network in both NLSM and LDM. The channel attention module and spatial attention module in the convolutional attention mechanism are sequentially introduced to enhance the attention to image lighting features and the efficiency of feature selection. The experimental results show the following points. ① MBLLEN, RUAS, zeroDCE, zeroDCE++, Retinex−Net, KinD++, and non-uniform illumination image enhancement algorithms are selected to enhance and qualitatively analyze images in various scenarios (underground transportation environment, single light source roadway, multi light source roadway, ore scenario). The analysis results indicate that non-uniform illumination image enhancement algorithms can avoid excessive enhancement of artificial light source areas. There is no halo or blurring phenomenon in the light source area, and colors are not prone to color deviation. The contrast is moderate, and the visual effect of the image is more realistic. ② The information entropy (IE), average gradient (AG), standard deviation (SD), naturalness image quality evaluator (NIQE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) are selected as evaluation indicators to quantitatively compare the quality of image enhancement images. The non-uniform illumination image enhancement algorithm is also in a relatively leading position in various scenarios. ③ The ablation experimental results show the non-uniform illumination image enhancement algorithm achieves optimal results on three evaluation indicators: NIQE, SSIM, and PSNR.