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• W4385485872 abstract "Blind face restoration is an extremely challenging task since it often relies on suitable reference priors, which are unavailable in real-world scenarios. Generative prior encapsulated in pre-trained face generator has shown its effectiveness in providing rich and realistic facial details. However, trained on a specific face dataset, face GAN prior mainly focus on face region but limited background information and local details. In this work, we propose a framework named DAGP-Face Restorer to perform restoration procedure by fusing multi-scale generative prior features and convolutional features extracted from degraded images. We first train a domain-alignment-guided encoder to project degraded inputs into the latent space aligning with that of high quality images, which allows our network to provide generative prior with better quality. Another encoder-decoder framework with feature fusion blocks is designed to extract convolutional features and fuse them with generative priors to output the final results. Experiment results show that, with the carefully design and fusion of two features, our model can better take into account restoration of face regions as well as image backgrounds and achieves superior performance in recovering faithful details and retaining fidelity." @default.
• W4385485872 created "2023-08-03" @default.
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• W4385485872 date "2023-03-17" @default.
• W4385485872 modified "2023-09-26" @default.
• W4385485872 title "DAGP-Face Restorer: Blind Face Restoration Network with Domain-aligned Generative Prior" @default.
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• W4385485872 doi "https://doi.org/10.1145/3594315.3594647" @default.
• W4385485872 hasPublicationYear "2023" @default.
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