Restore-R1: 通过多模态大语言模型感知反馈的强化学习图像修复代理
Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback
AI总结：
本文提出Restore-R1框架，利用强化学习和多模态大语言模型感知反馈，高效解决图像修复问题，无需标注数据即可实现高质量修复。
摘要：
复杂图像修复旨在从受多种退化影响的输入中恢复高质量图像。最近的修复代理通过视觉-语言模型和大语言模型提供有希望的修复能力，但因反射、回滚和迭代工具搜索存在显著效率瓶颈。此外，其性能严重依赖于退化识别模型，需要大量标注数据进行训练，限制了其在无标注环境中的应用。为了解决这些限制，我们提出了一种基于策略优化的修复框架，学习一个轻量级代理来确定工具调用序列。该代理在顺序决策过程中运行，每一步选择最合适的修复操作以最大化最终图像质量。为了在无标注环境中进行训练，我们引入了一种由多模态大语言模型驱动的新型奖励机制，这些模型充当对齐人类的评估者，为策略改进提供感知反馈。一旦训练完成，我们的代理执行确定性的修复计划，无需冗余的工具调用，显著加速推理同时保持高质量的修复效果。大量实验表明，尽管不使用监督，我们的方法在全参考指标上与SOTA性能相当，并在多种退化场景的无参考指标上超越现有方法。
英文摘要：
Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration agents, powered by vision-language models and large language models, offer promising restoration capabilities but suffer from significant efficiency bottlenecks due to reflection, rollback, and iterative tool searching. Moreover, their performance heavily depends on degradation recognition models that require extensive annotations for training, limiting their applicability in label-free environments. To address these limitations, we propose a policy optimization-based restoration framework that learns an lightweight agent to determine tool-calling sequences. The agent operates in a sequential decision process, selecting the most appropriate restoration operation at each step to maximize final image quality. To enable training within label-free environments, we introduce a novel reward mechanism driven by multimodal large language models, which act as human-aligned evaluator and provide perceptual feedback for policy improvement. Once trained, our agent executes a deterministic restoration plans without redundant tool invocations, significantly accelerating inference while maintaining high restoration quality. Extensive experiments show that despite using no supervision, our method matches SOTA performance on full-reference metrics and surpasses existing approaches on no-reference metrics across diverse degradation scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
