  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.
