Aiming at the problems of insufficient light, low contrast, and information loss in images taken by imaging devices at night or in low-light environments, an improved dark image enhancement network named RelightGAN is designed based on generative adversarial network (GAN). It contains two discriminators and one generator, and the generator is jointly constrained by two sets of adversarial losses and cyclic losses to generate a better illumination layer. To enhance the recovery of image details during network training, a residual network is introduced to solve the gradient vanishing problem. At the same time, a hybrid attention mechanism CBAM structure is introduced to increase the generator’s attention to important information and structures in the image, enhancing network expression capability. By comparing the image enhanced by RelightGAN with those enhanced by other dark image enhancement networks, the peak signal-to-noise ratio (PSNR) of the former is improved by 12.81% and the structural similarity (SSIM) is enhanced by 5.95%. Experimental results show that the RelightGAN network combines the advantages of traditional algorithms and neural networks to improve dark scene images and image visibility.