Aimed at the issue that the edge details of the dehazing images were insufficiently clear， and the majority of the existing U-Net dehazing networks did not adequately exploit the information in the frequency domain and neglected the information exchange among different channels， resulting in a blurry structure， a dual-scale fusion network with frequency-domain feature distillation was proposed for the effective dehazing of single images. In the Coarse-scale feature extraction subnet， a large-scale convolution kernel was utilized to extract image texture information， and a residual attention mechanism was employed to enhance the features related to haze. In the Fine-scale high-frequency fusion subnet， a high-frequency feature distillation module was devised to refine the extracted structure and edge information and gradually restore clear images. Meanwhile， the cross-fusion strategy was adopted to fuse the features of different channels. The experimental results indicate that compared with the MSTN algorithm（Efficient and Accurate Multi-Scale Topological Network）， the peak signal-to-noise ratio and structural similarity on the outdoor image dataset have been enhanced by 9.98% and 4.77% respectively. The experimental results on diverse datasets demonstrate that the proposed approach exhibits superior performance. This method can effectively enhance the dehazing effect， retain more structural information， and possess better color detail recovery capability.