Closely-Coupled Reconstruction and Generation for Blind Face Restoration

Blind Face Restoration (BFR) involves restoring high-quality images from various unknown and severely degraded counterparts, which is a challenging task due to the conflicting objectives of content reconstruction and detail generation. In this paper, we propose a closely-coupled approach to address this problem and achieve photorealistic and faithful reproductions. Specifically, we propose a two-step image restoration model that consists of the following steps: Firstly, we train a BaseNet that incorporates a filtered feature fusion module ($F^{3}M$) to purify degraded feature maps. Secondly, while keeping the BaseNet fixed, we train a DetailNet that utilizes a feature probabilistic model to generate high-frequency detail information. The proposed framework not only separates the reconstruction and generation processes but also deeply analyzes their interactions, leading to an optimized balance between perceptual quality and fidelity. Our approach is validated through extensive experiments on both synthetic datasets and real-world facial photographs, demonstrating significant improvements in Frechet Inception Distance (FID) scores while maintaining identity consistency. The experimental results highlight our method's state-of-the-art performance, achieving superior visual quality and processing efficiency compared to existing methods.