Recently, convolutional neural networks (CNNs) have been widely employed to
promote the face hallucination due to the ability to predict high-frequency
details from a large number of samples. However, most of them fail to take into
account the overall facial profile and fine texture details simultaneously,
resulting in reduced naturalness and fidelity of the reconstructed face, and
further impairing the performance of downstream tasks (e.g., face detection,
facial recognition). To tackle this issue, we propose a novel external-internal
split attention group (ESAG), which encompasses two paths responsible for
facial structure information and facial texture details, respectively. By
fusing the features from these two paths, the consistency of facial structure
and the fidelity of facial details are strengthened at the same time. Then, we
propose a split-attention in split-attention network (SISN) to reconstruct
photorealistic high-resolution facial images by cascading several ESAGs.
Experimental results on face hallucination and face recognition unveil that the
proposed method not only significantly improves the clarity of hallucinated
faces, but also encourages the subsequent face recognition performance
substantially. Codes have been released at
this https URL

提出了一种基于卷积神经网络的增强人脸超分辨率重建方法，通过分离面部结构信息和纹理细节信息实现外部和内部注意力组合，增强了面部结构和细节真实性并提高人脸识别表现。