Self-Supervised Face Image Restoration with a One-Shot Reference

• URL: http://arxiv.org/abs/2203.03005v5
• Date: Tue, 19 Dec 2023 13:15:00 GMT
• Title: Self-Supervised Face Image Restoration with a One-Shot Reference
• Authors: Yanhui Guo, Fangzhou Luo, Shaoyuan Xu
• Abstract summary: We propose a semantic-aware latent space exploration method for image restoration (SAIR) By explicitly modeling semantics information from a given reference image, SAIR is able to reliably restore severely degraded images.
• Score: 6.113093749947422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For image restoration, methods leveraging priors from generative models have been proposed and demonstrated a promising capacity to robustly restore photorealistic and high-quality results. However, these methods are susceptible to semantic ambiguity, particularly with images that have obviously correct semantics such as facial images. In this paper, we propose a semantic-aware latent space exploration method for image restoration (SAIR). By explicitly modeling semantics information from a given reference image, SAIR is able to reliably restore severely degraded images not only to high-resolution and highly realistic looks but also to correct semantics. Quantitative and qualitative experiments collectively demonstrate the superior performance of the proposed SAIR. Our code is available at https://github.com/Liamkuo/SAIR.

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