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).

MetaWeather 是一种针对任意天气条件的少样本天气恢复方法，通过匹配新天气条件下输入图像和示例图像之间的特征，利用元学习构建元知识以提供灵活的适应性，并通过参数高效的微调方法来避免过拟合问题。在 BID Task II.A 数据集上的实验表明，与最先进的图像恢复方法相比，我们的方法在峰值信噪比和结构相似性方面取得了最佳性能。