Fine-Grained Degradation Representations for Efficient Agentic Image Restoration

Existing agentic image restoration methods rely on coarse degradation labels, which cannot capture fine-grained characteristics within the same degradation type and may lead to inefficient tool selection. We propose Locality-prior Agentic Image Restoration (LAIR), an efficient framework that reuses successful restoration trajectories based on fine-grained degradation similarity. LAIR employs a Degradation-Query Momentum Contrastive Encoder (DQ-MCE), which combines learnable degradation queries, cross-content momentum contrastive learning, and parameter-aware regularization to learn content-robust yet degradation-sensitive representations. These representations enable the agent to retrieve, rank, execute, and verify relevant historical tool chains instead of repeatedly rolling back and rescheduling when restoration fails. Experiments on both synthetic and real-world degradations show that LAIR improves restoration fidelity while maintaining competitive perceptual quality. It also reduces runtime by up to 58% and requires fewer tool invocations than AgenticIR. These results demonstrate that fine-grained degradation representations provide an effective bridge between historical restoration trajectories and efficient agentic decision-making.