Blind Face Restoration (BFR) aims to recover high-quality face images from low-quality 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; 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 multi-prior 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 heatmaps), reference-level (facial dictionaries) and pixel-level (degraded images) information and thus generates faithful and realistic images. Quantitative and qualitative experiments show that MFPSNet performs favorably on both synthetic and real-world datasets against the state-of-the-art BFR methods. The codes are publicly available at: https://github.com/YYJ1anG/MFPSNet.

翻译：盲人脸部恢复(BFR)的目标是从低质量的图像中恢复高质量的脸部图像,并通常采用面部前端的面部图像来提高恢复性能。然而,目前的方法仍然有两大困难:(1) 如何在不进行广泛手调的情况下建立强大的网络结构;(2) 如何在一个网络中从多个面部前端获取补充信息,以改进恢复性能。为此,我们提议建立一个面部恢复搜索网络(FRSNet),以便在我们指定的搜索空间内对合适的特征提取结构进行适应性搜索,这可以直接促进恢复性。 在FRSNet的基础上,我们进一步设计多面部前端搜索网络(MFPSNet),采用多面部前端学习计划。MFPSNet最理想地从不同面部前端提取信息,并将信息整合成图像功能,确保外部指导和内部特征都得到保留。 MFPSNet(FSNet)充分利用语层层层层(定位地图)、地测量级(地热测),参考级别(酸字典)和平级JFS-Q级(dealimal-FS-S-deal-deal-S-S-deal-FS-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-SD-SD-SD-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S