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 are available at https://github.com/TaoWangzj/GridFormer.
@article{arxiv.2305.17863,
title = {GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions},
author = {Tao Wang and Kaihao Zhang and Ziqian Shao and Wenhan Luo and Bjorn Stenger and Tong Lu and Tae-Kyun Kim and Wei Liu and Hongdong Li},
journal= {arXiv preprint arXiv:2305.17863},
year = {2024}
}