Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution
Real-world image restoration (IR) remains challenging due to complex and coupled degradations. While recent agentic IR frameworks leverage Large Language Models for flexible tool planning, they face two critical limitations. First, from a search scheme perspective, excessive reliance on greedy strategies fails to balance exploration and exploitation. Second, existing agentic systems underutilize information, exhibiting episodic amnesia. To address these challenges, we propose \textbf{Self-Evolving Agentic Image Restoration (SEAR)}, which formulates restoration as a sequential decision-making problem. Inspired by the dual-process theory, SEAR comprises an Intuitive Executor and a Deliberat...
| Authors | Shuang Cui, Fan Ji, Guanglong Sun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 June 2026 |
| Last updated at source | 7 September 2026 |
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