  Real-world weather conditions are intricate and often occur concurrently.
However, most existing restoration approaches are limited in their
applicability to specific weather conditions in training data and struggle to
generalize to unseen weather types, including real-world weather conditions. To
address this issue, we introduce MetaWeather, a universal approach that can
handle diverse and novel weather conditions with a single unified model.
Extending a powerful meta-learning framework, MetaWeather formulates the task
of weather-degraded image restoration as a few-shot adaptation problem that
predicts the degradation pattern of a query image, and learns to adapt to
unseen weather conditions through a novel spatial-channel matching algorithm.
Experimental results on the BID Task II.A, SPA-Data, and RealSnow datasets
demonstrate that the proposed method can adapt to unseen weather conditions,
significantly outperforming the state-of-the-art multi-weather image
restoration methods.
