In order to solve the problem of haze weather affecting image quality, this paper proposed a two-branch feature fusion image dehazing algorithm. Firstly, the data fitting branch of dense residual form increased the network depth and extracted high-frequency detail features. The knowledge transfer branch of U-Net form provided supplemental knowledge to the finite data. Then the multi-scale fusion module adaptively fused feature of two branches to recover high-quality dehazing images. In addition, brightness constraint was introduced to combined loss function to assign higher weights to the dense haze region. Finally, both synthetic and real-world datasets were used for testing and compared with existing dehazing algorithms such as FFA and GCANet. Experimental results showed that the proposed algorithm had good dehazing effect both on synthetic and real hazy images. And compared with other comparison algorithms, the average value of peak signal to noise ratio on four nonhomogeneous haze datasets was increased by 1.55 dB?10.30 dB and the average value of structural similarity was increased by 0.0312?0.2440.