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arXiv · 2407.13719

HazeCLIP: Towards Language Guided Real-World Image Dehazing

Ruiyi Wang·Wenhao Li·Xiaohong Liu·Chunyi Li·Zicheng Zhang·Xiongkuo Min·Guangtao Zhai
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Abstract

Existing methods have achieved remarkable performance in image dehazing, particularly on synthetic datasets. However, they often struggle with real-world hazy images due to domain shift, limiting their practical applicability. This paper introduces HazeCLIP, a language-guided adaptation framework designed to enhance the real-world performance of pre-trained dehazing networks. Inspired by the Contrastive Language-Image Pre-training (CLIP) model's ability to distinguish between hazy and clean images, we leverage it to evaluate dehazing results. Combined with a region-specific dehazing technique and tailored prompt sets, the CLIP model accurately identifies hazy areas, providing a high-quality, human-like prior that guides the fine-tuning process of pre-trained networks. Extensive experiments demonstrate that HazeCLIP achieves state-of-the-art performance in real-word image dehazing, evaluated through both visual quality and image quality assessment metrics. Codes are available at https://github.com/Troivyn/HazeCLIP.

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Ruiyi Wang, Wenhao Li, Xiaohong Liu, Chunyi Li, Zicheng Zhang, Xiongkuo Min, Guangtao Zhai. 2024-07-18. HazeCLIP: Towards Language Guided Real-World Image Dehazing. https://arxiv.org/abs/2407.13719

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