API: Empowering Generalizable Real-World Image Dehazing via Adaptive Patch Importance Learning

• URL: http://arxiv.org/abs/2601.01992v1
• Date: Mon, 05 Jan 2026 10:53:41 GMT
• Title: API: Empowering Generalizable Real-World Image Dehazing via Adaptive Patch Importance Learning
• Authors: Chen Zhu, Huiwen Zhang, Yujie Li, Mu He, Xiaotian Qiao,
• Abstract summary: We introduce a novel Adaptive Patch Importance-aware (API) framework for generalizable real-world image dehazing.<n>Specifically, our framework consists of an Automatic Haze Generation (AHG) module and a Density-aware Haze Removal (DHR) module.<n>To alleviate the ambiguity of the dehazed image details, we further introduce a new Multi-Negative Contrastive Dehazing (MNCD) loss.
• Score: 12.516890497421203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when applied to complex real-world hazy scenes, primarily due to limited training data and the intrinsic complexity of haze density distributions.To address these challenges, we introduce a novel Adaptive Patch Importance-aware (API) framework for generalizable real-world image dehazing. Specifically, our framework consists of an Automatic Haze Generation (AHG) module and a Density-aware Haze Removal (DHR) module. AHG provides a hybrid data augmentation strategy by generating realistic and diverse hazy images as additional high-quality training data. DHR considers hazy regions with varying haze density distributions for generalizable real-world image dehazing in an adaptive patch importance-aware manner. To alleviate the ambiguity of the dehazed image details, we further introduce a new Multi-Negative Contrastive Dehazing (MNCD) loss, which fully utilizes information from multiple negative samples across both spatial and frequency domains. Extensive experiments demonstrate that our framework achieves state-of-the-art performance across multiple real-world benchmarks, delivering strong results in both quantitative metrics and qualitative visual quality, and exhibiting robust generalization across diverse haze distributions.

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