Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs. Conventional approaches predominantly rely on learning feature representations from ground-truth (GT) data; however, inherent imperfections in GT datasets constrain restoration performance to the mean quality level of the training data, rather than attaining maximally attainable visual quality. To overcome this limitation, we propose a novel framework that incorporates an Image Quality Prior (IQP) derived from No-Reference Image Quality Assessment (NR-IQA) models to guide the restoration process toward optimal HQ reconstructions. Our methodology synergizes this IQP with a learned codebook prior through two critical innovations: (1) During codebook learning, we devise a dual-branch codebook architecture that disentangles feature extraction into universal structural components and HQ-specific attributes, ensuring comprehensive representation of both common and high-quality facial characteristics. (2) In the codebook lookup stage, we implement a quality-conditioned Transformer-based framework. NR-IQA-derived quality scores act as dynamic conditioning signals to steer restoration toward the highest feasible quality standard. This score-conditioned paradigm enables plug-and-play enhancement of existing BFR architectures without modifying the original structure. We also formulate a discrete representation-based quality optimization strategy that circumvents over-optimization artifacts prevalent in continuous latent space approaches. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques across multiple benchmarks. Besides, our quality-conditioned framework demonstrates consistent performance improvements when integrated with prior BFR models. The code will be released.

翻译：盲人脸复原旨在解决将退化的低质量人脸图像重建为高质量输出的挑战。传统方法主要依赖于从真实数据中学习特征表示；然而，真实数据集中固有的不完美性将复原性能限制在训练数据的平均质量水平，而非达到可实现的最高视觉质量。为克服这一局限，我们提出了一种新颖框架，该框架引入了一种源自无参考图像质量评估模型的图像质量先验，以引导复原过程朝向最优的高质量重建。我们的方法通过两项关键创新，将此图像质量先验与一个学习得到的码本先验协同结合：（1）在码本学习阶段，我们设计了一种双分支码本架构，将特征提取解耦为通用结构成分和高质量特定属性，确保了对常见及高质量人脸特征的全面表示。（2）在码本查找阶段，我们实现了一个基于Transformer的质量条件化框架。源自无参考图像质量评估的质量分数作为动态条件信号，引导复原朝向最高可行的质量标准。这种分数条件化的范式能够即插即用地增强现有的盲人脸复原架构，而无需修改其原始结构。我们还提出了一种基于离散表示的质量优化策略，规避了连续潜在空间方法中普遍存在的过度优化伪影。大量实验表明，我们的方法在多个基准测试中超越了现有最先进技术。此外，当与先前的盲人脸复原模型集成时，我们的质量条件化框架展现出了一致的性能提升。代码将予以发布。