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Although generative facial prior and geometric prior have recentlydemonstrated high-quality results for blind face restoration, producingfine-grained facial details faithful to inputs remains a challenging problem.Motivated by the classical dictionary-based methods and the recent vectorquantization (VQ) technique, we propose a VQ-based face restoration method -VQFR. VQFR takes advantage of high-quality low-level feature banks extractedfrom high-quality faces and can thus help recover realistic facial details.However, the simple application of the VQ codebook cannot achieve good resultswith faithful details and identity preservation. Therefore, we furtherintroduce two special network designs. 1). We first investigate the compressionpatch size in the VQ codebook and find that the VQ codebook designed with aproper 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 paralleldecoder consisting of a texture decoder and a main decoder. Those two decodersthen interact with a texture warping module with deformable convolution.Equipped with the VQ codebook as a facial detail dictionary and the paralleldecoder design, the proposed VQFR can largely enhance the restored quality offacial details while keeping the fidelity to previous methods.

VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder