Learning Degradation for Real-World Face Super-Resolution

Acquiring degraded faces with corresponding high-resolution (HR) faces is critical for real-world face super-resolution (SR) applications. To generate low-resolution (LR) faces with degradation similar to those in real-world scenarios, most approaches learn a deterministic mapping from HR faces to LR faces. However, these deterministic models fail to model the various degradation of real-world LR faces, which limits the performance of the following face SR models. In this work, we learn a degradation model based on conditional generative adversarial networks (cGANs). Specifically, we propose a simple and effective weight-aware content loss that adaptively assigns different content losses to LR faces generated from the same HR face under different noise vector inputs. It significantly improves the diversity of the generated LR faces while having similar degradation to real-world LR faces. Compared with previous degradation models, the proposed degradation model can generate HR-LR pairs, which can better cover various degradation cases of real-world LR faces and further improve the performance of face SR models in real-world applications. Experiments on four datasets demonstrate that the proposed degradation model can help the face SR model achieve better performance in both quantitative and qualitative results.