Degradation-Aware Blind Face Restoration via High-Quality VQ Codebook

Blind face restoration, as a kind of face restoration method dealing with complex degradation, has been a challenging research hotspot recently. However, due to the influence of a variety of degradation in low-quality images, artifacts commonly exist in the low fidelity results of existing methods, resulting in a lack of natural and realistic texture details. In this paper, we propose a degradation-aware blind face restoration method based on a high-quality vector quantization (VQ) codebook to improve the degradation-aware capability and texture quality. The overall framework consists of Degradation-aware Module (DAM), Texture Refinement Module (TRM) and Global Restoration Module (GRM). DAM adopts the channel attention mechanism to adjust the weight of feature components in different channels, so that it has the ability to perceive complex degradation from redundant information. In TRM, continuous vectors are quantized and replaced with high-quality discretized vectors in the VQ codebook to add texture details. GRM adopts the reverse diffusion process of the pre-trained diffusion model to restore the image globally. Experiments show that our method outperforms state-of-the-art methods on synthetic and real-world datasets.