All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on degradation-specific representations, often resulting in oversmoothing and artifacts. To address this, we propose ClearAIR, a novel AiOIR framework inspired by Human Visual Perception (HVP) and designed with a hierarchical, coarse-to-fine restoration strategy. First, leveraging the global priority of early HVP, we employ a Multimodal Large Language Model (MLLM)-based Image Quality Assessment (IQA) model for overall evaluation. Unlike conventional IQA, our method integrates cross-modal understanding to more accurately characterize complex, composite degradations. Building upon this overall assessment, we then introduce a region awareness and task recognition pipeline. A semantic cross-attention, leveraging semantic guidance unit, first produces coarse semantic prompts. Guided by this regional context, a degradation-aware module implicitly captures region-specific degradation characteristics, enabling more precise local restoration. Finally, to recover fine details, we propose an internal clue reuse mechanism. It operates in a self-supervised manner to mine and leverage the intrinsic information of the image itself, substantially enhancing detail restoration. Experimental results show that ClearAIR achieves superior performance across diverse synthetic and real-world datasets.

翻译：多合一图像复原（AiOIR）技术已取得显著进展，为复杂的真实世界退化问题提供了有前景的解决方案。然而，现有方法大多严重依赖退化特定的表征，往往导致过度平滑和伪影问题。为此，我们提出ClearAIR——一种受人类视觉感知（HVP）启发、采用分层式由粗到精复原策略的新型AiOIR框架。首先，利用早期HVP的全局优先特性，我们采用基于多模态大语言模型（MLLM）的图像质量评估（IQA）模型进行整体评价。与传统IQA不同，我们的方法融合跨模态理解，能更准确地表征复杂的复合退化类型。在此整体评估基础上，我们进一步提出区域感知与任务识别流程。通过语义引导单元驱动的语义交叉注意力机制，首先生成粗粒度语义提示。在此区域上下文引导下，退化感知模块隐式捕获区域特定的退化特征，从而实现更精确的局部复原。最后，为恢复精细细节，我们提出内部线索复用机制。该机制以自监督方式挖掘并利用图像自身的内在信息，显著提升细节复原质量。实验结果表明，ClearAIR在多种合成与真实世界数据集上均取得了优越性能。