ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration
Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang

TL;DR
ClearAIR introduces a hierarchical, human-visual-perception-inspired framework for all-in-one image restoration, utilizing multimodal assessment, semantic guidance, and self-supervised detail enhancement to outperform existing methods.
Contribution
The paper presents a novel AiOIR framework inspired by human visual perception, integrating multimodal IQA, semantic cross-attention, and self-supervised internal clue reuse for improved image restoration.
Findings
Achieves superior performance on synthetic and real-world datasets.
Effectively captures complex degradations with region-aware modules.
Enhances detail restoration through self-supervised internal clue reuse.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsImage and Video Quality Assessment · Advanced Image Processing Techniques · Image Enhancement Techniques
