Although the single image dehazing algorithms based on the deep convolutional neural network have made significant progress，there are still some problems, such as poor visibility and artifacts. To overcome these shortcomings，we present a single image dehazing network, taking the encoder-decoder structure as the basic frame and combining the attention mechanism and residual dense block. First，the scheme integrates an encoder, a feature recovery module and the decoder to directly predict the clear images. Then, the residual dense block with attention mechanism is introduced into the dehazing network so as to improve the network's feature extraction ability. Finally, based on the attention mechanism, an adaptive skip connection module is proposed to enhance the network recovering ability for the clear images’ details. Experimental results show that the proposed dehazing network provides better dehazing results on synthetic datasets and real-world images.