Semantic-Aware Latent Space Exploration for Face Image Restoration
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For image restoration, most existing deep learning based methods tend to overfit the training data leading to bad results when encountering unseen degradations out of the assumptions for training. To improve the robustness, generative adversarial network (GAN) prior based methods have been proposed, revealing a promising capability to restore photo-realistic and high-quality results. But these methods suffer from semantic confusion, especially on semantically significant images such as face images. In this paper, we propose a semantic-aware latent space exploration method for image restoration (SAIR). By explicitly modeling referenced semantics information, SAIR can consistently restore severely degraded images not only to high-resolution highly-realistic looks but also to correct semantics. Quantitative and qualitative experiments collectively demonstrate the effectiveness of the proposed SAIR. Our code can be found in https://github.com/Liamkuo/SAIR.
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