SemFSR: An Unsupervised Face SR with Semantic Features for Multiple Degradations

Abstract

Face Super-Resolution (FSR) field has witnessed significant progress with the development of deep learning, which is also widely applied in high-level vision tasks as the preprocessing step. Recently, FSR methods are exploring to utilize facial priors in the reconstruction of High-Resolution (HR) faces. However, facial priors directly extracted from Low-Resolution (LR) faces are less accurate or even unavailable. Meanwhile, the generalization ability of FSR methods can be further improved across different degradations, e.g., manual interpolated degradations, and real-world degradation with noise and blur. To tackle these problems, we propose a coarse-to-fine unsupervised FSR method based on semantic features called SemFSR under multiple degradations. The SemFSR contains the Degradation Stage and the Generation Stage. The Degradation Stage learns to generate degraded LR faces interfered with noise and blur. At the Generation Stage, we firstly reconstruct the "Coarse-SR" face from the degraded LR face for more accurate semantic features. Then we further propose the Channel Attention Block with Semantic features (CAB-S) and Semantic Loss to reconstruct the "Fine-SR" face. Both quantitative and qualitative experiments demonstrate the superiority of our SemFSR when encountering multiple degraded LR faces, compared with the state-of-the-art supervised and unsupervised FSR methods.