Haze reduces the clarity and details of the image, and thus the impact on subsequent visual information processing is a challenging issue. Although the existing image dehazing algorithm can remove the haze, it is less effective for processing outdoor dense fog scenes. In order to break through the limitations of the existing defogging algorithm, this research combined with the atmospheric scattering physical model, proposed an end-to-end multi-scale parallel fusion defogging network. The network uses multi-scale convolution to extract features of different scales from the whole to the part, and fuse these features in parallel multiple times. In addition, by introducing a residual module to carry out in-depth learning of detailed features, more image details can be recovered. Experimental results and data analysis show that the proposed method can exhibit good defogging performance on both synthetic and real images, with PSNR and SSIM indicators increasing by an average of 3% year-on-year.