Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i.e., parsing map) directly from low-resolution face image for the following utilization. To exploit the extracted prior fully, a parsing map attention fusion block is carefully designed, which can not only effectively explore the information of parsing map, but also combines powerful attention mechanism. Moreover, in light of that high-resolution features contain more precise spatial information while low-resolution features provide strong contextual information, we hope to maintain and utilize these complementary information. To achieve this goal, we develop a multi-scale refine block to maintain spatial and contextual information and take advantage of multi-scale features to refine the feature representations. Experimental results demonstrate that our method outperforms the state-of-the-arts in terms of quantitative metrics and visual quality. The source codes will be available at https://github.com/wcy-cs/FishFSRNet.

翻译：人脸超分辨率是一种将低分辨率人脸图像转换为对应高分辨率图像的技术。本文构建了一种新型解析图引导的人脸超分辨率网络，该网络可直接从低分辨率人脸图像中提取人脸先验（即解析图）用于后续处理。为充分利用所提取的先验信息，我们精心设计了解析图注意力融合模块，该模块不仅能有效挖掘解析图信息，还结合了强大的注意力机制。此外，考虑到高分辨率特征包含更精确的空间信息，而低分辨率特征提供丰富的上下文信息，我们希望保持并利用这些互补信息。为此，我们开发了多尺度精炼模块以保持空间和上下文信息，并利用多尺度特征优化特征表示。实验结果表明，本方法在量化指标和视觉质量上均优于当前最优方法。源代码将发布于 https://github.com/wcy-cs/FishFSRNet。