To solve the problem of speck noise and artifacts in optical coherence tomography (OCT) image acquisition, a PSRGAN model is proposed and a transfer learning method is combined to improve the reconstruction quality of OCT retinal images. The PSRGAN model was based on the super-resolution generative adversary network (SRGAN) composed of generator and discriminator, and the improved PECA module is added to the discriminator, which can fully capture the spatial information of multi-scale feature maps and realize the cross-dimensional channel feature interaction of images. As for the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and edge retention index (EPI), the proposed method had better results in comparison with the best performance PSRGAN–TL–X-ray network by 2.19% and 4.07%, 10.64%, respectively. The results show that the proposed method significantly improves the image quality and automatic segmentation effect compared with other methods.