Aiming at the problems of edge blurring and loss of edge details that occur in the convolutional neural network image dehazing method when dealing with the image edge texture, in this study, a single image dehazing network design method, guided by multilevel edge a priori information, is proposed. The design method integrates an edge feature extraction block, an edge feature fusion block, and a dehazing feature extraction block, which performs rich edge feature extraction on foggy images and reconstructs the edge image. Furthermore, the edge feature fusion block efficiently fuses the edge a priori information with the context information of the foggy image at multiple levels. Then, the dehazing feature extraction block performs multiscale deep feature extraction on the image and adds attention mechanism to the important channels. A large number of experiments are conducted on the RESIDE dataset and compared with the mainstream dehazing methods, in which the peak signal-to-noise ratio and structural similarity index measurement of the indoor dataset reach 37.58 and 0.991, respectively. Additionally, the number of parameters and amount of computation are only 2.024×10⁶ and 24.84×10⁹, which shows that the method in this study effectively defogs the image while reducing the number of parameters and amount of computation. Moreover, the method exhibits good performance and edge detail preservation ability.