Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language Models
<|reference_start|>Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language Models: This paper addresses the limitations of adverse weather image restoration approaches trained on synthetic data when applied to real-world scenarios. We formulate a semi-supervised learning framework employing vision-language models to enhance restoration performance across diverse adverse weather conditions in real-world settings. Our approach involves assessing image clearness and providing semantics using vision-language models on real data, serving as supervision signals for training restoration models. For clearness enhancement, we use real-world data, utilizing a dual-step strategy with pseudo-labels assessed by vision-language models and weather prompt learning. For semantic enhancement, we integrate real-world data by adjusting weather conditions in vision-language model descriptions while preserving semantic meaning. Additionally, we introduce an effective training strategy to bootstrap restoration performance. Our approach achieves superior results in real-world adverse weather image restoration, demonstrated through qualitative and quantitative comparisons with state-of-the-art works.<|reference_end|>
@article{xu2024towards,
    title={Towards Real-World Adverse Weather Image Restoration: Enhancing
  Clearness and Semantics with Vision-Language Models},
    author={Jiaqi Xu, Mengyang Wu, Xiaowei Hu, Chi-Wing Fu, Qi Dou, Pheng-Ann Heng},
    journal={arXiv preprint arXiv:2409.02101},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02101},
    primaryClass={cs.CV cs.MM}
}