  Due to various and complicated snow degradations, single image desnowing is a
challenging image restoration task. As prior arts can not handle it ideally, we
propose a novel transformer, SnowFormer, which explores efficient
cross-attentions to build local-global context interaction across patches and
surpasses existing works that employ local operators or vanilla transformers.
Compared to prior desnowing methods and universal image restoration methods,
SnowFormer has several benefits. Firstly, unlike the multi-head self-attention
in recent image restoration Vision Transformers, SnowFormer incorporates the
multi-head cross-attention mechanism to perform local-global context
interaction between scale-aware snow queries and local-patch embeddings.
Second, the snow queries in SnowFormer are generated by the query generator
from aggregated scale-aware features, which are rich in potential clean cues,
leading to superior restoration results. Third, SnowFormer outshines advanced
state-of-the-art desnowing networks and the prevalent universal image
restoration transformers on six synthetic and real-world datasets. The code is
released in \url{https://github.com/Ephemeral182/SnowFormer}.
