To address the problems of uneven illumination distribution, missing edge details, and noise interference in underground coal mine images, a mine image enhancement algorithm based on local perception and multi-domain guided fusion is proposed. First, the algorithm constructs a dynamic nonlinear luminance mapping function based on local luminance features to adaptively adjust the enhancement amplitude. This addresses the issues of overexposure in strong light areas and missing details in dark areas caused by uneven illumination. Second, an adaptive gradient enhancement strategy is introduced to construct a gradient weight matrix. This matrix dynamically allocates enhancement weights according to local luminance differences, thereby suppressing noise and sharpening edges while maintaining luminance balance, achieving the collaborative optimization of luminance and details. Finally, a saturation stretching module based on color drift perception is designed to correct color deviations. Combined with a non-local means (NLM) denoising mechanism in the YUV space, it further improves the overall perceptual quality and color naturalness of the images. Extensive experimental validations were conducted on the public Low-Light(LOL) test set and a self-built coal mine image dataset. Quantitative evaluation results show that the proposed method achieves the best overall performance in both full-reference metrics (e.g., Peak Signal-to-Noise Ratio(PSNR), Structural Similarity Index Measure(SSIM)) and no-reference metrics (e.g., Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)). Compared with the evaluated methods, it more effectively improves image contrast, preserves structural details, and suppresses noise. Ultimately, this method effectively improves the luminance uniformity, contrast, and edge detail resolution of images in low-light environments, providing a feasible theoretical reference for downstream tasks such as image enhancement and target detection in coal mine intelligent monitoring systems.