The issue of infrared image deblurring has been a significant concern. However, in some specific scenes, the current mainstream deblurring algorithms based on optimization or deep learning fail to provide satisfactory results. Aiming to address the ineffectiveness of deep learning methods due to the low-cost datasets' unavailability for specific scenes, we innovatively propose a relatively simple full-chain imaging degradation simulation method using ground-to-air aircraft infrared imaging scene as an example, which considers the effects of blur and noise caused by the atmosphere, imaging system, target motion and detector. Through this method, we could generate abundant blur-clear image pairs by altering various parameters. To enhance the neural network's generalization ability and the deblurring performance in the specific scenes, we employ a two-step approach: pretraining on the public GoPro dataset and subsequent finetuning on the simulation dataset. After testing on the simulation dataset and some real-world images, we have discovered the importance of selecting a pretraining dataset that closely matches the scene degradation mode. Additionally, regardless of whether the model is pre-trained on the UIRD or GoPro dataset, there are significant enhancements in the deblurring effect following finetuning with our constructed simulation dataset. In summary, compared to the traditional deconvolution methods and the methods trained on a general dataset, our approach not only exhibits superior deblurring capabilities but also effectively mitigates noise and prevents the occurrence of artifactual textures such as ringing artifact.