Aiming at the problem that the traditional dehazing algorithm is easy to rely on prior knowledge and the restored clear image produces color distortion,this paper proposes a haze image clarity algorithm based on dual attention mechanism.Firstly,the haze image is input into the encoder,and the feature image is obtained after down-sampling.The feature extraction module connects several basic blocks of feature extraction together,and each basic block is composed of local residual learning and feature attention module,which improves the image quality and the utilization rate of image feature information,and increases the stability of network training.Then feature images are processed by parallel structure of channel attention and multi-scale spatial attention,which makes the network pay more attention to detail features, extract more key information,and improve network efficiency.Finally,the fused feature image is input into the decoder,and the haze density image matching the input size is obtained by multi-level mapping.The experimental results show that the algorithm presented in this paper can effectively remove haze and get more natural and clear images no matter for synthetic or real hazy images.