HazeCLIP: Towards Language Guided Real-World Image Dehazing

• URL: http://arxiv.org/abs/2407.13719v1
• Date: Thu, 18 Jul 2024 17:18:25 GMT
• Title: HazeCLIP: Towards Language Guided Real-World Image Dehazing
• Authors: Ruiyi Wang, Wenhao Li, Xiaohong Liu, Chunyi Li, Zicheng Zhang, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: Existing methods have achieved remarkable performance in single image dehazing, particularly on synthetic datasets. This paper introduces HazeCLIP, a language-guided adaptation framework designed to enhance the real-world performance of pre-trained dehazing networks.
• Score: 62.4454483961341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing methods have achieved remarkable performance in single 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 utilize it to evaluate dehazing results. Combined with a region-specific dehazing technique and tailored prompt sets, 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 the state-of-the-art performance in real-word image dehazing, evaluated through both visual quality and no-reference quality assessments. The code is available: https://github.com/Troivyn/HazeCLIP .

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