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

Understand

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.