Leveraging vision-language prompts for real-world image restoration and enhancement

Yanyan Wei, Yilin Zhang, Kun Li, Fei Wang, Shengeng Tang, Zhao Zhang

Abstract: Highlights•We propose a vision-language-prompted model for real-world adverse weather removal.•We use data augmentation to address the problem of learning from real-world datasets.•Experiments shows that the proposed method outperforms existing methods.•Our work provides inspiration for the design of real-world image restoration models.