Recent reference-based face restoration methods have received considerable
attention due to their great capability in recovering high-frequency details on
real low-quality images. However, most of these methods require a high-quality
reference image of the same identity, making them only applicable in limited
scenes. To address this issue, this paper suggests a deep face dictionary
network (termed as DFDNet) to guide the restoration process of degraded
observations. To begin with, we use K-means to generate deep dictionaries for
perceptually significant face components (\ie, left/right eyes, nose and mouth)
from high-quality images. Next, with the degraded input, we match and select
the most similar component features from their corresponding dictionaries and
transfer the high-quality details to the input via the proposed dictionary
feature transfer (DFT) block. In particular, component AdaIN is leveraged to
eliminate the style diversity between the input and dictionary features (\eg,
illumination), and a confidence score is proposed to adaptively fuse the
dictionary feature to the input. Finally, multi-scale dictionaries are adopted
in a progressive manner to enable the coarse-to-fine restoration. Experiments
show that our proposed method can achieve plausible performance in both
quantitative and qualitative evaluation, and more importantly, can generate
realistic and promising results on real degraded images without requiring an
identity-belonging reference. The source code and models are available at
https://github.com/csxmli2016/DFDNet.

该论文提出了一种深度字典网络（称为 DFDNet），用于参考基础面部修复方法中没有限制同一身份高质量参考图像的场景，并通过字典功能转移块从高质量图像中生成字典以修复真实降质图像上的细节。