In order to solve the problems of detail blurring and color deviation of images processed by the existing dehazing methods, a dual-attention dehazing network based on feature enhancement is proposed.In this network, an encoder-decoder structure is used to design a dual-attention feature enhancement module, in which the Ghost module is used to replace the nonlinear convolution to realize the lightweight processing of the model.The Receptive Field Block (RFB) fully integrates the characteristics of different scales.Dual attention mechanism is introduced to realize cross-channel and spatial interaction of information, so as to ensure the performance of the model and suppress the noise features.The RESIDE dataset is used for network training and testing.The experimental results show that the proposed algorithm has excellent performance in both subjective visual and objective evaluation indicators, which can effectively improve the feature extraction ability of the network, realize the color restoration of foggy images in different scenes, and enhance the contrast and clarity of the image.