To solve the problem of insufficient detail processing in the existing image super-resolution reconstruction algorithm, a super-resolution reconstruction algorithm of images based on improved enhanced super-resolution generative adversarial network (ESRGAN) is proposed. Firstly, the deep information extraction module of the improved ESRGAN generation network is improved using multiscale dense block (MDB) instead of dense block (DB), and by adding channel attention mechanism after MDB to adjust the characteristic response values of different channels. Secondly, the shallow feature extraction module of the improved ESRGAN model is used to extract the original features of the low resolution images, and the deep information extraction module is used to extract the depth residual features of the low resolution images. The original features and the depth residual features are fused by adding the corresponding elements. Finally, the reconstruction module is used to complete the image super-resolution reconstruction. The proposed algorithm's two and four times super-resolution reconstructions are tested on Set5, Set14, and BSD100 datasets and compared to Bicubic, FSRCNN, and ESRGAN methods. The results show that the proposed algorithm's reconstructed image has a clearer edge, and it can provide more details, which greatly improves the image's visual effect. Compared to ESRGAN, the proposed algorithm improves the average peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of 2-fold super-resolution reconstruction images by 0. 467 dB and 0. 005, respectively; At the same time, the proposed algorithm improves the average PSNR and SSIM of 4-fold super-resolution reconstruction images by 0.438 dB and 0.015, respectively.