The recent advancements in face image restoration and super-resolution have seen a significant shift towards leveraging diffusion models and human-AI collaboration. These approaches aim to enhance the quality of degraded images while preserving identity-specific features and meeting human perceptual standards. Innovations such as single-step diffusion models and attention-sharing mechanisms have enabled faster and more personalized restoration processes, addressing the challenges of diverse degradation types and real-time processing demands. Additionally, the integration of human perception priors and multi-modality supervision has improved semantic consistency and perceptual naturalness in super-resolution tasks. Privacy-preserving face recognition has also advanced, with methods focusing on disrupting global features while enhancing local features to achieve effective recognition in black-box environments. These developments collectively represent a substantial leap forward in the field, offering scalable solutions and state-of-the-art performance in various image restoration and super-resolution applications.