Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration under adverse weather conditions. GridFormer is designed in a grid structure using a residual dense transformer block, and it introduces two core designs. First, it uses an enhanced attention mechanism in the transformer layer. The mechanism includes stages of the sampler and compact self-attention to improve efficiency, and a local enhancement stage to strengthen local information. Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer. This design further improves the network's ability to learn effective features from both preceding and current local features. The GridFormer framework achieves state-of-the-art results on five diverse image restoration tasks in adverse weather conditions, including image deraining, dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The source code and pre-trained models will be released.

翻译：摘要：恶劣天气条件下的图像恢复是计算机视觉中的一项艰巨任务。本文提出一种名为GridFormer的新型Transformer框架，作为恶劣天气条件下图像恢复的骨干网络。GridFormer采用网格结构设计，基于残差密集Transformer模块，并引入两个核心设计。首先，在Transformer层中使用增强的注意力机制，该机制包含采样器与紧凑自注意力阶段以提升效率，以及局部增强阶段以强化局部信息。其次，引入残差密集Transformer模块（RDTB）作为GridFormer的最终层。该设计进一步提升了网络从前序与当前局部特征中学习有效特征的能力。GridFormer框架在五种不同的恶劣天气图像恢复任务中取得了最先进的结果，包括去雨、去雾、去雨与去雾联合、去雪以及多天气恢复。源代码与预训练模型将公开发布。