ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration

Xu Zhang; Huan Zhang; Guoli Wang; Qian Zhang; Lefei Zhang

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arXiv · 2601.02763

ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration

Xu Zhang·Huan Zhang·Guoli Wang·Qian Zhang·Lefei Zhang

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Abstract

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.

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Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang. 2026-03-21. ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration. https://arxiv.org/abs/2601.02763

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