Abstract: This study proposes a super-resolution reconstruction algorithm for infrared gray images based on deep residual neural networks. Initially, the residual convolution module is employed to deepen the network, enhancing its learning capacity. This enables the convolutional layer to utilize more neighborhood information during learning, resulting in improved ability to process complex scenes. Subsequently, we used the skip connection method to increase the high-frequency information transmission to enhance the image details. Experimental results show that the proposed network can effectively enrich the details of the reconstructed image, and that the target contour in the reconstructed image is significantly improved.