In order to improve the image quality of uncooled infrared thermal imager, and meet the needs of viewing and locking in low contrast and dim-area, a super-resolution reconstruction model of infrared image based on multi-scale dense residual network is proposed in this paper. The basic framework of the model is to reconstruct high-resolution image by cascading multiple residual features. Firstly, a multi-scale cross-channel fusion module is proposed. By fusing the branch results of different receptive fields, it not only fuses the complementary information of different receptive fields, but also helps to improve the gradient convergence and feature transmission. Then, multiple cross-fusion modules are cascaded and optimized by residual feature learning to learn high-resolution detail information. The simulation results show that the super-resolution model proposed in this paper can achieve better super-resolution reconstruction effect, and has better performance in weak structure maintenance and point target maintenance. Our proposed model has achieved highquality super-resolution reconstruction on the embedded deep learning platform of Hisilicon, and has high engineering application value.