Face super-resolution aims to recover high-resolution face images with accurate geometric structures. Most of the conventional super-resolution methods are trained on paired data that is difficult to obtain in the real-world setting. Besides, these methods do not fully utilize facial prior knowledge for face super-resolution. To tackle these problems, we propose an end-to-end unsupervised face super-resolution network to super-resolve low-resolution face images. We propose a gradient enhancement branch and a semantic guidance mechanism. Specifically, the gradient enhancement branch reconstructs high-resolution gradient maps, under the restriction of two proposed gradient losses. Then the super-resolution network integrates features in both image and gradient space to super-resolve face images with geometric structure preservation. Moreover, the proposed semantic guidance mechanism, including a semantic-adaptive sharpen module and a semantic-guided discriminator, can reconstruct sharp edges and improve local details in different facial regions adaptively, under the guidance of semantic parsing maps. Qualitative and quantitative experiments demonstrate that our proposed method can reconstruct high-resolution face images with sharp edges and photo-realistic details, outperforming the state-of-the-art methods.

人脸超分辨率旨在恢复具有精确几何结构的高分辨率人脸图像。大多数传统的超分辨率方法都是在现实世界中难以获得的配对数据上进行训练的。此外，这些方法没有充分利用面部先验知识进行面部超分辨率。为了解决这些问题，我们提出了一个端到端的无监督人脸超分辨率网络来超分辨率低分辨率人脸图像。我们提出了一个梯度增强分支和一个语义指导机制。具体来说，梯度增强分支在两个提议的梯度损失的限制下重建高分辨率梯度图。然后超分辨率网络将图像和梯度空间中的特征整合到具有几何结构保留的超分辨率人脸图像中。而且，所提出的语义指导机制，包括语义自适应锐化模块和语义引导鉴别器，可以在语义解析图的指导下自适应地重建锐利边缘并改善不同面部区域的局部细节。定性和定量实验表明，我们提出的方法可以重建具有锐利边缘和逼真细节的高分辨率人脸图像，优于最先进的方法。