Multi-scale Attention Network for Single Image Super-Resolution
This work addresses super-resolution for image processing applications, presenting an incremental improvement by adapting existing attention mechanisms to ConvNets.
The authors tackled single image super-resolution by proposing a multi-scale attention network (MAN) that combines multi-scale mechanisms with large kernel attention, achieving performance on par with SwinIR while offering trade-offs between state-of-the-art results and computational costs.
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.