To address the low resolution problem of infrared images in the medical field, we build a distributed array–based infrared imaging system with a simple structure and a real-time performance that achieved an improved image resolution using image algorithm processing. The proposed system is used to obtain four images with pixel-level displacement. One of the images is used as a benchmark, and the other three images are registered. Then, the projection of convex set algorithm is used to reconstruct the images and obtain a high-resolution infrared image. Finally, the reconstruction method of a generative admissible neural network is employed to obtain the infrared super-resolution image. Experimental results show that the infrared imaging system with a distributed array camera can realize real-time super-resolution image reconstruction, and the infrared image resolution can be improved from 400 × 300 to 3200×2400 (an eightfold increment). Compared with the original image, the mean and standard deviation of the super-resolution reconstructed image increase by 1.86% and 8.67%, while the entropy value remains basically unchanged. The proposed image processing algorithm realizes the super-resolution reconstruction for infrared images, which meets the application requirements of infrared super-resolution imaging in the medical field.