Combined Swin Transformer and UNet for GAN face inpainting
Most of the GAN-based face restoration techniques use CNN for restoration,ignoring the global information and overall uniformity of face restoration,which leads to unsatisfactory restoration results.Based on this problem,a joint Swin Trans-former and UNet GAN face repair algorithm is proposed for face image repair.The method adopts the GAN generator-discriminator architecture as a whole,using Swin Transformer as the backbone network for capturing the global dependencies of the image,and the encoding-decoding structure of UNet for feature extraction and reconstruction in the local region.The experimental results show that the method can better handle the face image restoration task compared to previous methods.
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