To address the problem that existing image deraining algorithms cannot well preserve the image background details,a multi-stage image deraining network based on highly efficient channel attention is proposed.Firstly,the network uses 3×3 convolution to extract shallow features of the rain map and passes them on to the Highly Efficient Channel Attention Block (HECAB),assigning different weights to different feature channels.Then,it is transfered to three parallel stages.In the first two stages,the encoder-decoder is used for multiscale feature extraction to reduce rain pattern information loss,where the Transformer block is used to suppress useless information transfer.Finally,in the third stage,the initial resolution block is used to replace the encoder-decoder,thus preserving the fine features of the output image.The experimental results show that：1） The structural similarities of the proposed algorithm on the public test sets of Rain800,Rain12,Rain100L and Rain100H are 0.830,0.968,0.960 and 0.944,and the peak signal-to-noise ratios are 27.33 dB,35.27 dB,36.79 dB and 28.94 dB；and 2） Compared with classical and novel image deraining algorithms,the proposed algorithm has better results in removing rain patterns and recovering background details.