IAMAgent: Toward an Interactive and Adaptive Multi-Agent System for Image Restoration

Existing image restoration and enhancement (IRE) methods suffer from three fundamental limitations: 1) they present a high technical barrier, requiring expert knowledge and lacking intuitive natural language control; 2) they are inflexible and poorly adaptable, as models are typically designed for single, specific degradations and fail on complex or mixed real-world scenarios; and 3) they lack interactivity and ignore subjectivity, operating as "closed-box" tools that cannot incorporate human feedback or understand nuanced user intentions. To overcome these challenges, we pioneer a novel paradigm: a Multi-Agent System (MAS) for interactive and adaptive image restoration. We design and implement a prototype system, Interactive and Adaptive Multi-Agent System (IAMAgent), which orchestrates a team of specialized agents to collaboratively solve complex IRE tasks. At its core, a Manager Agent, driven by a Large Language Model, interprets user commands, devises strategies, and allocates sub-tasks. It directs a Perception Agent for degradation diagnosis, a suite of specialized Execution Agents that encapsulate various low-level vision models, and a Critique Agent for automated quality assessment. This collaborative framework enables an innovative, language-driven, and human-in-the-loop optimization process. Our work is the first to introduce the MAS paradigm to the IRE domain, transforming it from a collection of static tools into a dynamic, user-centric, and intelligent system. We demonstrate that IAMAgent not only significantly enhances restoration performance and adaptability but also bridges the critical gap between high-level human intention and low-level vision tasks.