To solve the problem of low-resolution infrared images affecting viewing and aiming in the photoelectric field, a lightweight multi-scale aggregation network is proposed to enhance the resolution of the central region when the IR image is zoomed. First, the algorithm uses scale kernels of different sizes to extract feature information and employs a shallow residual structure to effectively aggregate local multi-scale residual features, thereby obtaining stronger feature representation capability. Then, a channel attention layer based on contrast perception is used to aggregate more multi-scale feature information. Finally, a high-resolution infrared image with rich detail and clarity is reconstructed. Simulation results show that the zooming method can extract fine multi-scale feature information without introducing additional parameters and can produce clear reconstruction results.