HazeCLIP: Towards Language Guided Real-World Image Dehazing
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913643369594880 |
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| author | Wang, Ruiyi Li, Wenhao Liu, Xiaohong Li, Chunyi Zhang, Zicheng Min, Xiongkuo Zhai, Guangtao |
| author_facet | Wang, Ruiyi Li, Wenhao Liu, Xiaohong Li, Chunyi Zhang, Zicheng Min, Xiongkuo Zhai, Guangtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HazeCLIP: Towards Language Guided Real-World Image Dehazing Wang, Ruiyi Li, Wenhao Liu, Xiaohong Li, Chunyi Zhang, Zicheng Min, Xiongkuo Zhai, Guangtao Computer Vision and Pattern Recognition 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. |
| title | HazeCLIP: Towards Language Guided Real-World Image Dehazing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.13719 |