This research introduces a novel chain-of-thought image restoration agent that can address multiple complex image degradations simultaneously without sacrificing performance.

• Leverages a multimodal LLM to analyze degradation types and select optimal restoration strategies
• Uses quality assessment to guide the restoration process through iterative refinement
• Demonstrates superior performance across various degradation types including noise, blur, and compression artifacts
• Provides better generalization to unseen degradation types compared to specialized models

This engineering breakthrough matters because it addresses a fundamental challenge in image processing: the trade-off between specialization and generalization in restoration tasks, enabling more robust visual content enhancement for real-world applications.

Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model