To solve the problems of low-illumination image, such as low brightness, low contrast, severe information loss, and color distortion, a low-illumination image enhancement algorithm was proposed based on a parallel residual network. The main function of the network model involved parallelizing the alternating residual module with the local-global residual module, using the improved loss function to calculate the test set loss, constantly adjusting the network parameters, and finally achieving a network model with strong enhancement ability. Experimental results show that the proposed model can effectively improve the brightness and contrast of the image and reduce the loss of edge details. Both synthetic and real image datasets were used in experiments. The subjective vision of our model is more natural, and the objective evaluation index is better than those of other contrast algorithms.