  Transformer is beneficial for image denoising tasks since it can model
long-range dependencies to overcome the limitations presented by inductive
convolutional biases. However, directly applying the transformer structure to
remove noise is challenging because its complexity grows quadratically with the
spatial resolution. In this paper, we propose an efficient Dual-branch
Deformable Transformer (DDT) denoising network which captures both local and
global interactions in parallel. We divide features with a fixed patch size and
a fixed number of patches in local and global branches, respectively. In
addition, we apply deformable attention operation in both branches, which helps
the network focus on more important regions and further reduces computational
complexity. We conduct extensive experiments on real-world and synthetic
denoising tasks, and the proposed DDT achieves state-of-the-art performance
with significantly fewer computational costs.
