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:人脸超分辨率是将低分辨率的人脸图像转换成对应的高分辨率图像的技术。本文提出了一种新颖的基于解析图的超分辨网络,该网络可直接从低分辨率人脸图像中提取面部先验(即解析图)以供后续利用。为了充分利用提取的先验,我们设计了一个解析图注意力融合块,它不仅能有效地探索解析图的信息,还结合了有力的注意力机制。此外,鉴于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了强大的上下文信息,我们希望保持并利用这些互补信息。为了实现这个目标,我们开发了多尺度细化块来保持空间和上下文信息,并利用多尺度特征来细化特征表示。实验结果表明,我们的方法在定量指标和视觉质量方面均优于当前最先进的方法。源代码将可在 https://github.com/wcy-cs/FishFSRNet 上获取。