  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.
