Key-Graph Transformer for Image Restoration
CoRR(2024)
Abstract
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.
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