论文标题
雪诺（Snowformer）：上下文相互作用变压器具有尺度意识的单图像
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
论文作者
论文摘要
由于各种复杂而复杂的积雪降解，因此否定单图像是一项具有挑战性的图像修复任务。由于先前的艺术无法理想地处理它，因此我们提出了一种新颖的变压器Snowformer，该变压器探索了有效的交叉分离，以在跨贴片之间构建本地全球环境互动，并超过了使用本地操作员或香草变压器的现有作品。与先前的否定方法和通用图像恢复方法相比，雪诺福特具有多个好处。首先，与最近的图像恢复视觉变压器中的多头自我注意力不同，雪诺形雪符（Snowformer）结合了多头跨注意机制，以在比例吸引人的雪查询和局部斑点嵌入之间执行局部全球环境相互作用。其次，雪形的雪查询是由汇总量表感知特征的查询发生器生成的，这些特征富含潜在的干净线索，从而导致较高的恢复结果。第三，Snowformer在六个合成和现实世界数据集上超出了先进的最先进的网络和普遍的通用图像恢复变压器。该代码在\ url {https://github.com/ephemeral182/snowformer}中发布。
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}.
