论文标题
一声参考
Self-Supervised Face Image Restoration with a One-Shot Reference
论文作者
论文摘要
为了恢复图像，已经提出了利用生成模型的先验的方法，并证明了有望恢复光真逼真和高质量结果的有希望的能力。但是，这些方法容易受到语义歧义的影响，尤其是具有明显正确语义（例如面部图像）的图像。在本文中，我们提出了一种用于图像修复的语义意识潜在空间探索方法（SAIR）。通过从给定的参考图像中明确建模语义信息，Sair能够可靠地恢复严重降级的图像，不仅是高分辨率和高度逼真的外观，而且还可以纠正语义。定量和定性实验共同证明了所提出的SAIR的出色表现。我们的代码可在https://github.com/liamkuo/sair上找到。
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.
