Abstract: A super-resolution reconstruction method based on a degradation model is proposed, aiming at solving the problem of image quality degradation due to out-of-focus blurring during image acquisition of aerospace precision parts.First, a realistic image degradation model is designed, which includes various factors such as out-of-focus blur, downsampling and noise, and simulates diverse degradation situations in real applications by extending the degradation space.Based on this model, synthetic data are generated and a super-resolution reconstruction model is trained to improve the quality of out-of-focus images of precision parts.In order to extract image features, a new swin transformer-based image super-resolution network is proposed, stacking multiple residual swin transformer channel attention（RST-CA） modules for every two swin transformer modules to introduce the channel attention module.The experimental results show that it outperforms the traditional degradation model in recovering the out-of-focus image details of aerospace precision parts, especially in the reconstruction of part texture and microcircular hole details, which show obvious clarity improvement.In terms of objective evaluation metrics, compared with the traditional degradation model and classical super-segmentation methods（e.g.,EDSR,SRGAN,SwinIR,etc.）,the method shows an average enhancement of about 3.2 dB and 0.05 in the PSNR and SSIM metrics, and is especially superior in the detail recovery of micro-miniature hole structures.This study provides an effective technical path for out-of-focus recovery and super-resolution reconstruction of aerospace precision parts, which has a large application prospect and industrial application value.