In order to solve the problem of dehazing a single image, a new end-to-end network is proposed, which uses an improved multi-scale feature loop to generate a confrontation network. Unlike previous models, the proposed network does not rely on traditional atmospheric scattering models, and does not need to correspond to matching images during the training process, which greatly simplifies the training process. Next, a new type of multi-scale generator is designed, which uses a dual-channel fusion feature pyramid structure to extract the features in the image to the greatest extent, and introduces multiple global and local discriminators to improve network performance and image quality. Experimental results show that the proposed model can achieve good results on different datasets.