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arXiv · 2201.06374

RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs

Zhouxia Wang·Jiawei Zhang·Runjian Chen·Wenping Wang·Ping Luo
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Abstract

Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality.

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Zhouxia Wang, Jiawei Zhang, Runjian Chen, Wenping Wang, Ping Luo. 2022-01-17. RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs. https://arxiv.org/abs/2201.06374

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