U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance.