Abstract
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.
Abstract (translated)
本文针对利用合成数据训练的逆境图像修复方法的局限性，应用于现实场景。我们提出了一种采用视觉语言模型的半监督学习框架，以增强现实场景中多样逆境下的修复性能。我们的方法包括评估图像清晰度并提供语义使用视觉语言模型对现实数据，作为训练修复模型的监督信号。 对于清晰度增强，我们使用现实数据，采用视觉语言模型评估的双步骤策略进行。对于语义增强，我们在视觉语言模型描述中调整天气条件，同时保留语义意义。此外，我们引入了一种有效的训练策略来启动修复性能的恢复。 通过与最先进工作的定性和定量比较，我们的方法在现实场景逆境图像修复方面实现了卓越的结果。
URL
https://arxiv.org/abs/2409.02101
