Reconstruction with robustness: A semantic prior guided face super-resolution framework for multiple degradations

Abstract

Despite the rapid advancements made with the support of deep learning, Face Super-Resolution (FSR) methods still suffer from challenges under multiple degradations. These challenges significantly impede the practical applications of FSR methods in real-world scenarios. Incorporating facial priors could potentially relieve this issue. However, ground truth priors are not feasible in real-world applications, meanwhile the accuracy of predicted priors is difficult to guarantee, especially for low-resolution faces under multiple degradations. Hence, it is worth exploring how to effectively leverage facial priors for improving the robustness of FSR under multiple degradations. To tackle these problems, we propose RSemFace, a robust semantic prior guided FSR framework to reconstruct multiple degraded faces. In RSemFace, we design the Degradation Stage to synthesize multiple degraded low-resolution faces with a variety of interpolations, noise levels, blurring kernels, and even the real-world interference. The Generation Stage generates Coarse-SR faces, and extracts semantic features from the Coarse-SR as priors, which are used to the reconstruction of Fine-SR faces with the support of Semantic Feature Attention Blocks (SFABs) and Semantic Loss. Both quantitative and qualitative results show the better robustness of our RSemFace for content recovery and perceptual quality in simultaneously handling multiple degraded faces compared with other state-of-the-art methods. Lastly, faces reconstructed by RSemFace are proven to improve the high-level vision task due to better recovered identities.