OCT retinal images super-resolution reconstruction based on PSRGAN and transfer learning
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.
OCT imagessuper-resolutiongenerative adversarial networkstransfer learningpyramidal attention
NETL
NSTL
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