In order to solve the current problems of insufficient detail extraction and poor visual effect after high magnification reconstruction of face images, a super-resolution method is proposed for single images of faces based on generative adversarial networks. Channel attention is added to the generative network to extract richer facial details, and the idea of iterative up and down sampling layers in the depth inverse projection network is borrowed to make the reconstructed image with good visual effect after high magnification. For the discriminator network, the normalization layer, which would destroy the image contrast, is removed. The experimental results show that the reconstructed images are more realistic and the visual effects are improved compared with Bicubic, SRCNN, LapSRN and SRGAN.
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