论文标题
还原型：未基因的钥匙值对的高质量盲人恢复
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
论文作者
论文摘要
盲面修复是从未知的降解中恢复高质量的面部图像。由于面部图像包含丰富的上下文信息，因此我们提出了一种方法，还原图像，该方法探讨了完全空间的关注，以模拟上下文信息并超越了使用本地运营商的现有作品。与先前的艺术相比，还原构造器具有多个好处。首先，与以前视觉变压器（VIT）中传统的多头自我发作不同，还原构图结合了一个多头跨注意层，以学习损坏的查询与高质量的键值对之间的完全空间相互作用。其次，从重建为导向的高质量词典中取样了Resotreformer中的钥匙值对，其元素富含高质量的面部特征，专门针对面部重建，从而导致出色的恢复结果。第三，RestoreFormer的表现优于一个合成数据集和三个现实世界数据集上的先进的最新方法，并且可以产生具有更好视觉质量的图像。
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.
