Semantic-Aware Detail Enhancement for Blind Face Restoration

The goal of Blind Face Restoration is to recover high-quality images from low-quality images suffering from unknown degradations, posing a significantly challenging problem. In recent years, numerous BFR methods have been proposed, achieving significant success. However, faces possess a unique facial topology, and subtle differences in texture, slight structural imbalances, and minimal asymmetry are easily perceptible in the restored face images. Previous methods often struggle to generate realistically high-quality images from real-world low-quality images and fail to preserve fine features. To more effectively restore image details and textures, providing a more natural and realistic restoration effect, we integrate facial semantic information as prior knowledge into the blind face restoration task. We employ a multi-head cross-attention mechanism to simultaneously consider facial semantic information and context information for modeling. Additionally, we introduce a local detail enhancement module specifically designed to enhance the processing capability of details around the eyes and mouth. Experimental results indicate that our proposed method recovers facial images on synthetic and real datasets more realistically and with higher fidelity.