Face Super-Resolution (FSR) aims to recover high-resolution (HR) face images
from low-resolution (LR) ones. Despite the progress made by convolutional
neural networks in FSR, the results of existing approaches are not ideal due to
their low reconstruction efficiency and insufficient utilization of prior
information. Considering that faces are highly structured objects, effectively
leveraging facial priors to improve FSR results is a worthwhile endeavor. This
paper proposes a novel network architecture called W-Net to address this
challenge. W-Net leverages meticulously designed Parsing Block to fully exploit
the resolution potential of LR image. We use this parsing map as an attention
prior, effectively integrating information from both the parsing map and LR
images. Simultaneously, we perform multiple fusions in various dimensions
through the W-shaped network structure combined with the LPF(LR-Parsing Map
Fusion Module). Additionally, we utilize a facial parsing graph as a mask,
assigning different weights and loss functions to key facial areas to balance
the performance of our reconstructed facial images between perceptual quality
and pixel accuracy. We conducted extensive comparative experiments, not only
limited to conventional facial super-resolution metrics but also extending to
downstream tasks such as facial recognition and facial keypoint detection. The
experiments demonstrate that W-Net exhibits outstanding performance in
quantitative metrics, visual quality, and downstream tasks.

通过利用面部先验信息，提出了一种名为 W-Net 的新型网络架构，用于解决面部超分辨率问题，该架构充分利用了低分辨率图像的分辨率潜力，并通过使用预测图和低分辨率图像的融合模块实现多维度的融合，同时平衡了感知质量和像素准确性。实验证明 W-Net 在定量指标、视觉质量和下游任务方面表现出色。