论文标题
面对超分辨率，逐渐嵌入多尺度的面部先验
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors
论文作者
论文摘要
面部超分辨率（FSR）任务是从低分辨率输入中重建高分辨率的面部图像。最近的作品通过使用面部地标（例如面部地标），在这项任务上取得了成功。大多数现有方法更加关注全球形状和结构信息，但更少注意本地纹理信息，这使得它们无法及时恢复本地细节。在本文中，我们为面部超分辨率提出了一个新型的基于卷积网络的框架，该框架逐渐引入了全球形状和局部纹理信息。我们充分利用了经常性网络的中间输出，地标信息和面部动作单元（AUS）信息分别在第一和第二步的输出中提取，而不是低分辨率输入。此外，我们引入了AU分类结果作为面部细节恢复的新型定量指标。广泛的实验表明，我们提出的方法在图像质量和面部细节恢复方面显着优于最先进的FSR方法。
The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Recent works have achieved success on this task by utilizing facial priors such as facial landmarks. Most existing methods pay more attention to global shape and structure information, but less to local texture information, which makes them cannot recover local details well. In this paper, we propose a novel recurrent convolutional network based framework for face super-resolution, which progressively introduces both global shape and local texture information. We take full advantage of the intermediate outputs of the recurrent network, and landmarks information and facial action units (AUs) information are extracted in the output of the first and second steps respectively, rather than low-resolution input. Moreover, we introduced AU classification results as a novel quantitative metric for facial details restoration. Extensive experiments show that our proposed method significantly outperforms state-of-the-art FSR methods in terms of image quality and facial details restoration.
