Abstract
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.
Abstract (translated)
复杂图像恢复旨在从受模糊、噪声、雨滴及压缩伪影等多重退化影响的输入中重建高质量图像。近期基于视觉语言模型与大语言模型的恢复智能体虽展现出前景，却因反思、回退及迭代工具搜索等操作面临显著效率瓶颈。此外，其性能高度依赖需要大量标注数据训练的退化识别模型，限制了在无标注环境中的适用性。为应对这些局限，本文提出一种基于策略优化的恢复框架，通过学习轻量级智能体来确定工具调用序列。该智能体在序列决策过程中运作，每一步选择最合适的恢复操作以最大化最终图像质量。为实现在无标注环境中的训练，我们引入一种由多模态大语言模型驱动的新型奖励机制——此类模型作为与人类认知对齐的评估者，为策略优化提供感知反馈。训练完成后，我们的智能体执行确定性恢复方案，避免冗余工具调用，在保持高恢复质量的同时显著加速推理过程。大量实验表明，尽管无需任何监督信号，本方法在全参考指标上达到与当前最优技术相当的性能，并在多样退化场景的无参考指标上超越现有方法。
URL
https://arxiv.org/abs/2512.18599
