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.

翻译：针对图像复原任务，现有利用生成模型先验信息的方法已展现出稳健恢复逼真高质量结果的潜力。然而，这些方法易受语义歧义影响，尤其对于具有明显正确语义的图像（如人脸图像）而言更为突出。本文提出一种面向图像复原的语义感知潜空间探索方法（SAIR）。通过显式建模给定参考图像的语义信息，SAIR不仅能够将严重退化图像可靠地恢复为高分辨率、高逼真度的视觉效果，还能实现正确的语义复原。定量与定性实验共同证明了所提SAIR方法的优越性能。我们的代码已开源在https://github.com/Liamkuo/SAIR。