In the infrared image denoising task,the deep learning algorithms are highly dependent on synthetic noise and cannot remove the real infrared noise well since the real infrared noise images are difficult to obtain in large batches.In this paper,a network framework for infrared image denoising based on domain adaptation is proposed,which consists of an image translation module and two image denoising modules.Firstly,the image translation module is used to convert the synthetic infrared noise image to the real infrared noise image.Then,the converted image and the original image are used as the training data for the denoising module and the consistency loss function is used to make the two image denoising modules produce consistent results.Finally,the trained network framework is used for infrared image denoising task.The experiments show that the proposed algorithm has higher quantitative metrics and better visual quality on synthetic infrared noise datasets compared with BM3D,DnCNN and ADNet,and has the same excellent denoising effect on real infrared noise datasets.It is demonstrated that the algorithm has good generalization capabilities and is able to recover clear infrared images under real noise.