Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

Real-world image restoration (IR) remains challenging dueto complex and coupled degradations. While recent agentic IR frame-works leverage Large Language Models for flexible tool planning, theyface two critical limitations. First, from a search scheme perspective,excessive reliance on greedy strategies fails to balance exploration andexploitation. Second, existing agentic systems underutilize information,exhibiting episodic amnesia. To address these challenges, we proposeSelf-Evolving Agentic Image Restoration (SEAR), which formu-lates restoration as a sequential decision-making problem. Inspired by thedual-process theory, SEAR comprises an Intuitive Executor and a Delib-erate Planner, respectively following the fast-thinking System 1 and slow-thinking System 2 principles. The Deliberate Planner employs Pruning-Aware Monte Carlo Tree Search for long-horizon reasoning, utilizing ahybrid no-reference reward and a Multimodal Large Language Model(MLLM)-based tournament to prevent metric exploitation. Complemen-tarily, the Intuitive Executor leverages a self-evolving episodic memoryindexed by degradation-aware state fingerprints. This mechanism dis-tills expensive search trajectories into adaptive expertise, overcomingepisodic amnesia while progressively amortizing cold-start explorationcosts through memory reuse. Extensive experiments on synthetic andreal-world benchmarks demonstrate its strong perceptual and quantita-tive performance.