用视觉语言模型统一修复各种恶劣天气下的图像退化

Image restoration is critical for improving the quality of degraded images, which is vital for applications like autonomous driving, security surveillance, and digital content enhancement. However, existing methods are often tailored to specific degradation scenarios, limiting their adaptability to the diverse and complex challenges in real-world environments. Moreover, real-world degradations are typically non-uniform, highlighting the need for adaptive and intelligent solutions. To address these issues, we propose a novel vision-language-guided universal restoration (VL-UR) framework. VL-UR leverages a zero-shot contrastive language-image pre-training (CLIP) model to enhance image restoration by integrating visual and semantic information. A scene classifier is introduced to adapt CLIP, generating high-quality language embeddings aligned with degraded images while predicting degraded types for complex scenarios. Extensive experiments across eleven diverse degradation settings demonstrate VL-UR's state-of-the-art performance, robustness, and adaptability. This positions VL-UR as a transformative solution for modern image restoration challenges in dynamic, real-world environments.