ing at the problems of low image contrast, color imbalance, and noise in low-light conditions, a low-light image enhancement model based on multi-branch all convolutional neural network (MBACNN) is proposed. The model is an end-to-end model, including feature extraction module (FEM), enhancement module (EM), fusion module (FM), and noise extraction module (NEM). By training the synthesized low-light and high-definition image sample, the model parameters are continuously adjusted according to the loss value of the verification set to obtain the optimal model, and then the synthetic low-light image and the real low-light image are tested. Experimental results show that compared with traditional image enhancement algorithms, the proposed model can effectively improve image contrast, adjust color imbalance, and remove noise. Both subjective visual and objective image quality evaluation indicators are further improved.