  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.
