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.
@article{arxiv.2203.03005,
title = {Self-Supervised Face Image Restoration with a One-Shot Reference},
author = {Yanhui Guo and Fangzhou Luo and Shaoyuan Xu},
journal= {arXiv preprint arXiv:2203.03005},
year = {2023}
}