Abstract: Video surveillance is one of the important means of coal mine safety monitoring, and the poor quality of video surveillance images affects the accuracy of the video surveillance system due to the influence of illumination, dust and water mist, so it is of great significance to study the mine low-illumination image enhancement algorithm. To address the problems of texture feature loss and colour distortion during the enhancement of extremely dark low-illumination images in coal mines, an unsupervised mine low-illumination image enhancement algorithm EMGAN based on the improved EnlightenGAN is proposed. Firstly, in the generator network, the original U-Net model is replaced by the ResU-Net model, and the residual linking mechanism is added to enhance the feature transmission ability, effectively alleviate the gradient disappearance problem in training, retain the detail information in the image, and introduce an efficient multi-scale attention module for cross-space learning in the down-sampling phase of the network to improve the feature extraction ability of the network in complex environments; secondly, construct a dual discriminator network based on the PatchGAN, which discriminates the global and local regions of the image respectively, and efficiently balances the overall image brightness and contrast; finally, the joint loss function is designed to combine the perceptual loss and colour consistency loss to avoid colour distortion. Based on the self-constructed coal mine underground dataset for validation, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio, and structural similarity of the improved algorithm are improved by an average of 6.84%, 24.88%, 7.54%, 8.30%, 27.40%, and 10.85%, respectively. The experimental results show that the algorithm improves the brightness and contrast of the extremely dark and low-illumination image of the mine while retaining the texture feature information of the image and avoiding the colour distortion phenomenon, which effectively improves the quality of the image and provides a more reliable basis for the subsequent video monitoring and analysis.