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

2026-01-06

arXiv.org

AI Summary

This work addresses the limitations of existing all-in-one image restoration methods, which often suffer from over-smoothing and artifacts due to their reliance on degradation-specific representations and struggle with complex real-world degradations. Inspired by human visual perception mechanisms, we propose a coarse-to-fine hierarchical restoration framework. It first leverages a multimodal large language model for cross-modal image quality assessment, then performs task-adaptive restoration through semantic-guided, region-aware degradation modeling, and finally enhances fine detail recovery via a self-supervised internal cue reuse mechanism. To our knowledge, this is the first approach to integrate human visual perception principles into all-in-one image restoration. Extensive experiments demonstrate state-of-the-art performance across multiple synthetic and real-world datasets, with significant suppression of artifacts and notable improvements in perceptual detail quality.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• All-in-One Image Restoration
• Image Degradation
• Oversmoothing
• Artifacts
• Human Visual Perception

Innovation

• Human Visual Perception
• Multimodal Large Language Model
• All-in-One Image Restoration
• Region-aware Degradation Modeling
• Self-supervised Internal Clue Reuse