Multiprior Learning Via Neural Architecture Search for Blind Face Restoration

Blind face restoration (BFR) aims to recover high-quality (HQ) face images from low-quality (LQ) ones and usually resorts to facial priors for improving restoration performance. However, current methods still suffer from two major difficulties: 1) how to derive a powerful network architecture without extensive hand tuning and 2) how to capture complementary information from multiple facial priors in one network to improve restoration performance. To this end, we propose a face restoration searching network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality. On the basis of FRSNet, we further design our multiple facial prior searching network (MFPSNet) with a multiprior learning scheme. MFPSNet optimally extracts information from diverse facial priors and fuses the information into image features, ensuring that both external guidance and internal features are reserved. In this way, MFPSNet takes full advantage of semantic-level (parsing maps), geometric-level (facial heat maps), reference-level (facial dictionaries), and pixel-level (degraded images) information and, thus, generates faithful and realistic images. Quantitative and qualitative experiments show that the MFPSNet performs favorably on both synthetic and real-world datasets against the state-of-the-art (SOTA) BFR methods. The codes are publicly available at: https://github.com/YYJ1anG/MFPSNet.