Owing to technical limitations regarding the device and process, the resolution of infrared images is relatively low compared to that of visible images, and deficiencies occur such as blurred textural features. In this study, we proposed a super-resolution reconstruction method based on a deep convolutional neural network (CNN) for infrared images. The method improves the residual module, reduces the influence of the activation function on the information flow while deepening the network, and makes full use of the original information of low-resolution infrared images. Combined with an efficient channel attention mechanism and channel-space attention module, the reconstruction process selectively captures more feature information and facilitates a more accurate reconstruction of the high-frequency details of infrared images. The experimental results show that the peak signal-to-noise ratio (PSNR) of the infrared images reconstructed using this method outperforms those of the traditional Bicubic interpolation method, as well as the CNN-based SRResNet, EDSR, and RCAN models. When the scale factor is ×2 and ×4, the average PSNR values of the reconstructed images improved by 4.57 and 3.37 dB, respectively, compared with the traditional Bicubic interpolation method.