SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing

Understand

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.