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Aiming at the problem of image blurring caused by the inherent low-frequency loss of optical synthetic aperture, an improved super-resolution generative countermeasure net (SRGAN) is proposed for image restoration research. Firstly, the optical synthetic aperture image data set was constructed by MATLAB, and the data set was processed by data enhancement. Secondly, according to the design idea of ASPP network, the residual structure of multi-scale SRGAN generator was constructed. Finally, the restoration effect was compared with the traditional super-resolution reconstruction algorithms. Experimental results show that this algorithm can speed up the model convergence and improve the model"s ability to acquire fine-grained features of the image. It has a better restoration effect for optical synthetic aperture images.