Aiming at the problem that the image super-resolution reconstruction algorithm based on the deep neural network is easy to lose feature information during feature extraction, which leads to the lacks of texture and edge details in the reconstructed image, this paper proposes a U-shaped network image super-resolution reconstruction algorithm with multi-level information compensation. Firstly, a U-shaped network for image super-resolution reconstruction is designed, which performs multi-level feature extraction and channel compression on the input features through the lower channel branch, and fuses the compressed features through the bottom module and extracts the related features of different channels, and performs multi-level feature extraction and channel recovery on the compressed related features through the upper channel branch. Then, a multi-level information compensation model is designed, which compensates for the lost information in the channel compression process and the information that is difficult to recover in the channel recovery process of the U-shaped network. Finally, compared with the mainstream algorithms, the proposed algorithm is tested and analyzed by the test sets of Set5, Set14, BSD100 and Urban100 at different magnifications. The experimental results show that the proposed algorithm greatly improves the peak signal-to-noise ratio(PSNR)/structural similarity(SSIM) index and visual effect.