While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution. Furthermore, the self-attention mechanism in transformers is prone to considering unnecessary global cues from unrelated objects or regions, introducing computational inefficiencies. In response to these challenges, we introduce the Key-Graph Transformer (KGT) in this paper. Specifically, KGT views patch features as graph nodes. The proposed Key-Graph Constructor efficiently forms a sparse yet representative Key-Graph by selectively connecting essential nodes instead of all the nodes. Then the proposed Key-Graph Attention is conducted under the guidance of the Key-Graph only among selected nodes with linear computational complexity within each window. Extensive experiments across 6 IR tasks confirm the proposed KGT's state-of-the-art performance, showcasing advancements both quantitatively and qualitatively.
@article{arxiv.2402.02634,
title = {Key-Graph Transformer for Image Restoration},
author = {Bin Ren and Yawei Li and Jingyun Liang and Rakesh Ranjan and Mengyuan Liu and Rita Cucchiara and Luc Van Gool and Nicu Sebe},
journal= {arXiv preprint arXiv:2402.02634},
year = {2024}
}