Key-Graph Transformer for Image Restoration
<|reference_start|>Key-Graph Transformer for Image Restoration: 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.<|reference_end|>
@article{ren2024key-graph,
    title={Key-Graph Transformer for Image Restoration},
    author={Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita
  Cucchiara, Luc Van Gool, Nicu Sebe},
    journal={arXiv preprint arXiv:2402.02634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02634},
    primaryClass={cs.CV cs.LG eess.IV}
}