Towards General Real-World Image Restoration: AIGC-Supervised Learning with Knowledge Distillation

Real-world image restoration must accommodate complex variations across scenes while supporting deployment under limited computational resources. However, acquiring paired data is costly and challenging, and synthetic degradations cannot fully capture real image formation processes, limiting the generalization of restoration models to real-world scenarios. We propose a real-world image restoration framework that integrates generative supervision, a perturbation-aware structural correction module (PSR), and knowledge distillation without requiring real clean targets. An input-anchored local risk-constrained selection strategy combines local content constraints, candidate disagreement, and high-risk region aggregation to select a pseudo-GT target with lower proxy content risk from candidates of similar perceptual quality. The selected target is paired with the degraded input to train a restoration teacher. To address variations in pseudo-GT targets across generative models or generation batches, we introduce PSR after the restoration teacher. Using selected alternative targets to guide local teacher probing, PSR learns a shared correction mapping through restoration supervision, response-weighted consistency, and input content constraints. The composite teacher incorporating PSR is then distilled into a student model. We also construct the XSMax dataset with scene-disjoint training, validation, and test splits. Extensive experiments demonstrate that our method improves the perceptual quality and content fidelity of restored images while enhancing output stability under variations in generative supervision. The proposed framework provides a practical approach to cross-scene image restoration without real clean targets and deployment in resource-constrained environments.