煤矿井下粉尘和暗光等因素导致采集的图像质量低，而现有图像增强方法存在图像细节丢失、局部特征不清晰、无法消除噪声、去尘效果不理想等问题。针对上述问题，提出了一种基于去尘估计和多重曝光融合的煤矿井下图像增强方法。该方法通过尘化图像简易模型及暗原色理论，并引入自适应衰减系数估算出图像透射率，再根据透射率分布，通过尘化图像简易模型复原物体的原始图像，将煤矿井下图像中的粉尘去除；利用多重曝光融合算法为曝光不足的原始图像生成一组不同曝光比的图像，并引入权值矩阵将这些不同曝光比的图像与原始图像进行融合，有效提升暗光图像质量。实验结果表明：相较于直方图均衡法、带色彩恢复的Retinex（MSRCR）方法、改进Retinex方法，该方法在去尘及暗光增强方面效果较好，颜色还原度较高，白边和过曝等现象得到抑制，且增强后的图像平均对比度分别提升了169.00%，42.50%，10.88%，平均图像熵分别提升了51.80%，16.45%，8.99%，平均亮度顺序误差（LOE）分别降低了31.01%，16.94%，7.83%，同时该方法运算耗时最短。

Factors such as dust and dim light in coal mines lead to low quality of collected images. The existing image enhancement methods have problems such as loss of image details, unclear local features, inability to eliminate noise, and unsatisfactory dust removal effects. In order to solve the above problems, a coal mine underground image enhancement method based on dust removal estimation and multiple exposure fusion is proposed. This method uses a simplified model of dust image and dark primary color theory, and introduces an adaptive attenuation coefficient to estimate the image transmittance. Based on the transmittance distribution, the original image of the object is restored using the simplified model of dust image to remove dust from the coal mine underground image. The method uses a multiple exposure fusion algorithm to generate a set of images with different exposure ratios for underexposed original images, and introduces a weight matrix to fuse these images with the original image, effectively improving the quality of dim light images. The experimental results show that compared to the histogram equalization method, the multiple-scale Retinex with color restoration method (MSRCR), and the improved Retinex method, this method has better results in dust removal and dim light enhancement, with higher color restoration, suppressed white edges and overexposure. The average contrast of the enhanced images has increased by 62.78%, 29.82%, 9.8%, and the average image entropy has increased by 34.13%, 14.12%, and 8.25%, respectively. The average lightness order error (LOE) has been reduced by 40.9%, 20.39%, and 8.5%, respectively. This method has the shortest computational time.