In public security and other fields, high-resolution thermal infrared image can provide more scene details, and has a wide range of application requirements.However, high equipment cost limits the acquisition of high-resolution infrared images.In this paper, a multistage skip Deep Residual Convolutional Neural Network (DR-CNN) is designed to reconstruct high resolution infrared images by software super-resolution method.Multistage skip dual-channel attention residual blocks are used to increase the convolution depth to solve the problem of lack of correlation between convolution layers.In order to reduce the complexity of training and speed up the operation, Concat module is used to realize the local feature information fusion and the deconvolution layer is used to upsample the feature images directly from low resolution images to high resolution images.Compared with SRCNN, FSRCNN and ADSR, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) are used as evaluation indexes.Experimental results show that the DR-CNN algorithm proposed is superior to other algorithms, and the generated high-resolution images are rich in detail and clear.