HazeCLIP: Towards Language Guided Real-World Image Dehazing
<|reference_start|>HazeCLIP: Towards Language Guided Real-World Image Dehazing: 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 .<|reference_end|>
@article{wang2024hazeclip:,
    title={HazeCLIP: Towards Language Guided Real-World Image Dehazing},
    author={Ruiyi Wang, Wenhao Li, Xiaohong Liu, Chunyi Li, Zicheng Zhang,
  Xiongkuo Min, Guangtao Zhai},
    journal={arXiv preprint arXiv:2407.13719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13719},
    primaryClass={cs.CV}
}