Feature-Guided Blind Face Restoration with GAN Prior

Blind face restoration (BFR) aims to restore high-quality face images from inputs with complex degradation, which is key to extensive applications. Existing methods usually learn a black-box mapping to achieve the goal, which however often produce over-smoothed results. This work proposes a novel feature-guided framework via leveraging prior from a pre-trained Generative Adversarial Network (GAN) model to recover reasonable textures. Furthermore, we design a feature fusion module to guide the generator with low-level spatial content information in the degraded input, for the sake of holding the consistency on both facial structure and background details. The proposed method can be seamlessly integrated with a learned GAN through a simple yet effective principle to recover realistic results under complex degradation circumstances. Extensive comparisons demonstrate the superiority of our strategy over other state-of-the-art methods in terms of restoration quality and training cost.