In order to improve the effect of infrared image super-resolution reconstruction, an infrared image super-resolution reconstruction method based on the fusion of visible light and near-infrared light based on depth learning is proposed. The salient region detection model of infrared image is established by using the reflective characteristics and infrared radiation characteristics of infrared image; The edge contour features of the image are detected by the appearance difference level between the visible light and near-infrared images, and the fusion feature parameters of visible light and near-infrared light are extracted; According to different fusion levels, image signal level, pixel level, feature level and decision level are reconstructed to extract image edge, shape and texture features; According to the noise level of the feature distribution and the registration quality, the infrared image super-resolution reconstruction is realized by using the depth learning algorithm. The simulation test results show that the method has a strong ability to detect the salient features of infrared image reconstruction, and the image resolution is improved to 1 280×960 PPI, the template matching accuracy is 49. 4%, the peak signal to noise ratio PSNR value is higher than 36. 34 dB, and the structure similarity SSIM value is higher than 0. 972. The reconstruction effect is good, and it is more suitable for infrared image target feature recognition in specific scenes.