Multi-scale Attention Network for Single Image Super-Resolution

Yan Wang, Yusen Li, Gang Wang, Xiaoguang Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5950-5960

ConvNets can compete with transformers in high-level tasks by exploiting larger receptive fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale attention network (MAN) by coupling classical multi-scale mechanism with emerging large kernel attention. In particular we proposed multi-scale large kernel attention (MLKA) and gated spatial attention unit (GSAU). Through our MLKA we modify large kernel attention with multi-scale and gate schemes to obtain the abundant attention map at various granularity levels thereby aggregating global and local information and avoiding potential blocking artifacts. In GSAU we integrate gate mechanism and spatial attention to remove the unnecessary linear layer and aggregate informative spatial context. To confirm the effectiveness of our designs we evaluate MAN with multiple complexities by simply stacking different numbers of MLKA and GSAU. Experimental results illustrate that our MAN can perform on par with SwinIR and achieve varied trade-offs between state-of-the-art performance and computations.