Abstract: The image dehazing based on prior relies on the atmospheric scattering model， which is susceptible to incomplete defogging and color distortion. Based on deep learning， this paper proposes a multi-scale dense receptive domain GAN image dehazing algorithm. Firstly， a multi-scale learning generator network is constructed to extract local details and global information of images through three different scales for feature fusion. Then， receptive dense blocks are used to increase receptive fields and obtain rich context information， and the extracted feature maps are further refined in multiple receptive dense blocks. Then，a multi-scale GAN discriminator is used， which consists of two identical sub-discriminators D1 and D2， and the two sub-discriminators jointly guide the generator training. Finally， L1 loss， perception loss and adversarial loss are combined to design a multivariate loss function to converge the network. The proposed algorithm is evaluated subjectively and objectively on SOTS test sets. The experimental results show that the proposed algorithm achieves better results and effectively， which improves the phenomenon of incomplete dehazing.

Key words: image dehazing, GAN, receptive field block, multi-scale learning