The structure of infrared imaging systems and complexity of the imaging environment lead to complex types of noise during infrared image processing, which can seriously affect image quality. This paper first describes the structure of the infrared imaging system and source of image noise, further discussing traditional and improved algorithms for infrared image noise reduction from the perspective of space and frequency domains, air-frequency combination, and deep learning. In this study, we focused on deep learning noise reduction algorithms, in view of their broad application and excellent noise reduction effect. The classical noise reduction algorithm was selected to conduct noise reduction experiments on real noisy infrared images. Experiments show that the deep-learning algorithm surpasses the traditional algorithm in performance.