  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 .
