As the basic problem of image enhancement, image defogging has received extensive attention. It has become a challenging area of research. For the problems of color distortion, fog residue of defogging images in prior method and deep learning method, this paper proposed an image defogging algorithm based on attention mechanism for detail recovery. Firstly, the improved CBAM (convolutional block attention module) module is introduced to design the attention basic block and encapsulate the basic block into blocks. Secondly, to strengthen the information interaction ability within the block, dense connection residual blocks are introduced between the blocks. Finally, the detail recovery module is designed to recover the detail of the fog image to further reduce the impact of fog residue. Numerical simulation experiments show that the proposed algorithm achieves higher peak signal-to-noise ratio and structural similarity compared with the mainstream defogging algorithm on the RESIDE (realistic single image defogging) data set and better visual effects on real images on the RESIDE (realistic single image defogging) data set.