Aiming at the problems of single feature extraction method and insufficient feature extraction of middle layer in existing image super-resolution reconstruction techniques, a channel-separable residual network based on attention mechanism is proposed. Firstly, a diverse branch block is designed by using the idea of multi-scale convolution to fully extract the low-frequency information of the image. Secondly, channel compression is used to reduce dimension to simplify feature information, and coordinate attention mechanism is introduced to enhance local fusion features. The trunk network is focused on extracting high-frequency features while accelerating convergence through long and short jump connections. Finally, the high-resolution image is reconstructed by upsampling layer. The proposed algorithm is compared and analyzed on the public data sets of Set5, Set14, BSD100 and Urban100 in the super-resolution reconstruction field. On set5 dataset of ×2 reconstruction task, compared with DBPN, parameters is 2/5 of DBPN, the PSNR and SSIM are improved by 0.09 dB and 0.001 6 respectively. Experimental results show that the proposed algorithm can fully extract image features and achieve similar or even better reconstruction results than other large-scale models with fewer parameters.