RCNet: Recurrent Collaboration Network Guided by Facial Priors for Face Super-Resolution

Abstract

In this paper, we present a novel FSR method, called RCNet, which progressively improves the performance of FSR and Landmark Estimation (LE) via recurrent collaboration. In our approach, FSR and LE complement each other. Different from previous FSR methods that directly estimate the facial landmarks on the low-resolution face images, the proposed RCNet conducts LE on the super-resolution face image obtained through multiple iterations. Benefiting from the super-resolution face images, facial landmarks are precisely estimated, which boosts the performance of FSR in turn. Furthermore, we design a Component-Aware Fusion Module (CAFM) for better recovering facial details, which adaptively fine-tunes the estimated landmarks and groups them into face components to maximize the guiding role of the facial land-marks. In addition, the iterative feature aggregation is developed to preferably capture the information from the LR/SR face images. Experimental results show that the proposed RCNet outperforms the state-of-the-art methods in both quantitative and qualitative aspects for super-resolving very low-resolution faces.