The pictures taken on hazy days have color distortion and blurry details， which will affect the quality of the pictures to a certain extent. Many deep learning based methods have good results on synthetic homogeneous haze images， but they have poor results on the real nonhomogeneous dehazing dataset introduced in the latest NTIRE （New Trends in Image Restoration and Enhancement） challenge. The main reason is that the non-uniform distribution of haze is complicated， and the texture details are easily lost in the process of dehazing. Moreover， the sample number of this dataset is limited， which is easy to lead to overfitting. Therefore， a Conditional Generative Adversarial Network with Dual-Branch generators （DB-CGAN） was proposed. Among them， in one branch， with U-net used as the basic architecture， through the strategy of "Strengthen-Operate-Subtract"， enhancement modules were added to the decoder to enhance the recovery of features in the decoder， and the dense feature fusion was used to build enough connections for non-adjacent levels. In the other branch， a multi-layer residual structure was used to speed up the training of the network， and a large number of channel attention modules were concatenated to extract more high-frequency detailed features as many as possible. Finally， a simple and efficient fusion subnet was used to fuse the two branches. In the experiment， this model is significantly better than the previous Dark Channel Prior （DCP）， All-in-One Dehazing Network （AODNet）， Gated Context Aggregation Network （GCANet）， and Multi-Scale Boosted Dehazing Network （MSBDN） dehazing models in the evaluation index Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM）. Experimental results show that the proposed network has better performance on nonhomogeneous dehazing datasets.