Due to the complex terrain environment and limited illumination in coal mines， images acquired by video surveillance equipment often have problems such as insufficient brightness， low contrast， color distortion， and loss of detail information. To solve the above problems， a multi-scene low-light image enhancement algorithm of a coal mine based on MCGN （multi-scale calibrated gating network） is proposed. The algorithm is composed of illumination enhancement network， detail enhancement network， color correction network， and gating fusion network. Firstly， the illumination enhancement network estimates the illumination information through a pre-lighting module. On this basis， the illumination enhancement module with spatial enhanced attention is cascading to enhance the capture ability of the occluded area and the local dark area. Subsequently， a self-calibration module is introduced to further improve the overall exposure control ability of the image. Secondly， to preserve and enhance texture and edge details， a multi-level residual structure is designed to form a detail enhancement network， ensuring that important detail information is not lost. Furthermore， in view of the inherent color distortion of the image and the color distortion generated in the enhancement process， a color correction network and a color loss function are constructed. The codec structure is used to decouple the color image into a color histogram， and the natural light color characteristics are learned based on the color histogram to guide the color distribution correction. Finally， in order to realize the organic fusion of the output images of the three networks， a new gating mechanism is designed in the gated fusion network， which learns the optimal fusion weights end-to-end to achieve an effective balance of brightness enhancement， detail restoration， and color correction. Experimental results show that the proposed algorithm is effective in improving image brightness， enriching texture features， and restoring true color. At the same time， the algorithm has good multi-scene applicability and fast reasoning speed， which can meet the actual needs of coal mines and provide strong technical support for coal mine safety production.