Abstract: Underground coal mine surveillance images suffer from noise, low clarity, missing color, and texture information. Additionally, machine learning-based image enhancement methods face challenges in collecting paired low-light and normal-light image datasets. To address these issues, this paper proposes an improved Zero-Reference Deep Curve Estimation (Zero-DCE) model for enhancing low-light images in mines. The ReLU activation function in the Zero-DCE model was replaced with Leaky ReLU to accelerate model convergence and improve the efficiency of low-light image feature learning. A Convolutional Block Attention Module (CBAM) was introduced at the skip connections between the shallow and deep networks of the Zero-DCE model to enhance the model's ability to capture key image features. An Asymmetric Convolution Block (ACB) was incorporated into the shallow network to optimize the model's learning of local image features and its ability to represent fine details. A Cascaded Convolution Kernel (CCK) was employed in the deep network to reduce the number of model parameters and computational cost, thereby shortening the training time. Experimental validation was conducted using the LOL public dataset and a self-built mine dataset. The results showed that the improved Zero-DCE model outperformed typical image enhancement models in terms of Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Natural Image Quality Evaluator (NIQE), and Visual Information Fidelity (VIF). Specifically, on the self-built dataset, MSE and NIQE decreased by 16.25% and 2.93%, respectively, while PSNR, SSIM, and VIF increased by 2.87%, 1.87%, and 17.64%, respectively. The enhanced images exhibited superior visual quality, effectively improving brightness while preserving detailed texture information and significantly reducing noise. The inference time for a single image was 0.138 seconds, enabling real-time image enhancement.