Recent works based on deep learning and facial priors have succeeded in
super-resolving severely degraded facial images. However, the prior knowledge
is not fully exploited in existing methods, since facial priors such as
landmark and component maps are always estimated by low-resolution or coarsely
super-resolved images, which may be inaccurate and thus affect the recovery
performance. In this paper, we propose a deep face super-resolution (FSR)
method with iterative collaboration between two recurrent networks which focus
on facial image recovery and landmark estimation respectively. In each
recurrent step, the recovery branch utilizes the prior knowledge of landmarks
to yield higher-quality images which facilitate more accurate landmark
estimation in turn. Therefore, the iterative information interaction between
two processes boosts the performance of each other progressively. Moreover, a
new attentive fusion module is designed to strengthen the guidance of landmark
maps, where facial components are generated individually and aggregated
attentively for better restoration. Quantitative and qualitative experimental
results show the proposed method significantly outperforms state-of-the-art FSR
methods in recovering high-quality face images.

本文提出了一种基于深度学习和迭代协作的面部图像超分辨率方法，利用面部上的先验知识和注意力融合模块，相比现有的超分辨率方法取得显著性能提升。