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Transformer is beneficial for image denoising tasks since it can modellong-range dependencies to overcome the limitations presented by inductiveconvolutional biases. However, directly applying the transformer structure toremove noise is challenging because its complexity grows quadratically with thespatial resolution. In this paper, we propose an efficient Dual-branchDeformable Transformer (DDT) denoising network which captures both local andglobal interactions in parallel. We divide features with a fixed patch size anda fixed number of patches in local and global branches, respectively. Inaddition, we apply deformable attention operation in both branches, which helpsthe network focus on more important regions and further reduces computationalcomplexity. We conduct extensive experiments on real-world and syntheticdenoising tasks, and the proposed DDT achieves state-of-the-art performancewith significantly fewer computational costs.

DDT: Dual-branch Deformable Transformer for Image Denoising