  Although generative facial prior and geometric prior have recently
demonstrated high-quality results for blind face restoration, producing
fine-grained facial details faithful to inputs remains a challenging problem.
Motivated by the classical dictionary-based methods and the recent vector
quantization (VQ) technique, we propose a VQ-based face restoration method -
VQFR. VQFR takes advantage of high-quality low-level feature banks extracted
from high-quality faces and can thus help recover realistic facial details.
However, the simple application of the VQ codebook cannot achieve good results
with faithful details and identity preservation. Therefore, we further
introduce two special network designs. 1). We first investigate the compression
patch size in the VQ codebook and find that the VQ codebook designed with a
proper compression patch size is crucial to balance the quality and fidelity.
2). To further fuse low-level features from inputs while not "contaminating"
the realistic details generated from the VQ codebook, we proposed a parallel
decoder consisting of a texture decoder and a main decoder. Those two decoders
then interact with a texture warping module with deformable convolution.
Equipped with the VQ codebook as a facial detail dictionary and the parallel
decoder design, the proposed VQFR can largely enhance the restored quality of
facial details while keeping the fidelity to previous methods.
