Real-world vision tasks frequently suffer from the appearance of adverse weather conditions including rain, fog, snow, and raindrops in captured images. Recently, several generic methods for restoring weather-degraded images have been proposed, aiming to remove multiple types of adverse weather effects present in the images. However, these methods have considered weather as discrete and mutually exclusive variables, leading to failure in generalizing to unforeseen weather conditions beyond the scope of the training data, such as the co-occurrence of rain, fog, and raindrops. To this end, weather-degraded image restoration models should have flexible adaptability to the current unknown weather condition to ensure reliable and optimal performance. The adaptation method should also be able to cope with data scarcity for real-world adaptation. This paper proposes MetaWeather, a few-shot weather-degraded image restoration method for arbitrary weather conditions. For this, we devise the core piece of MetaWeather, coined Degradation Pattern Matching Module (DPMM), which leverages representations from a few-shot support set by matching features between input and sample images under new weather conditions. In addition, we build meta-knowledge with episodic meta-learning on top of our MetaWeather architecture to provide flexible adaptability. In the meta-testing phase, we adopt a parameter-efficient fine-tuning method to preserve the prebuilt knowledge and avoid the overfitting problem. Experiments on the BID Task II.A dataset show our method achieves the best performance on PSNR and SSIM compared to state-of-the-art image restoration methods. Code is available at (TBA).

翻译：现实世界中的视觉任务常因捕获图像中出现雨、雾、雪及雨滴等恶劣天气条件而受到影响。近期，多种通用型天气退化图像恢复方法被提出，旨在消除图像中的多种类型恶劣天气效应。然而，这些方法将天气视为离散且互斥的变量，导致其无法泛化至训练数据范围之外的未知天气条件（例如雨、雾与雨滴的共存情况）。为此，天气退化图像恢复模型应具备对当前未知天气条件的灵活适应性，以确保可靠且最优的性能表现。该适应方法还需应对现实场景中数据稀缺的问题。本文提出MetaWeather——一种面向任意天气条件的少样本天气退化图像恢复方法。为此，我们设计了MetaWeather的核心模块，即退化模式匹配模块（DPMM），该模块通过将输入图像与样本图像在新天气条件下的特征进行匹配，从而利用少样本支持集的表征信息。此外，我们在MetaWeather架构基础上构建基于情景元学习的元知识体系，以提供灵活的适应性。在元测试阶段，我们采用参数高效微调方法，以保留预构建知识并避免过拟合问题。在BID Task II.A数据集上的实验表明，与现有最优图像恢复方法相比，本方法在PSNR和SSIM指标上取得了最佳性能。代码将于（待定）开源。