Papers › Multi-scale Attention Network for Single Image Super-Resolution
Multi-scale Attention Network for Single Image Super-Resolution
Yan Wang, Yusen Li, Gang Wang, Xiaoguang Liu
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | Set14 - 4x upscaling | MAN+ | PSNR | 29.12 | #23 of 104 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 4x upscaling | MAN+ | SSIM | 0.7941 | #23 of 104 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 4x upscaling | MAN | PSNR | 29.07 | #25 of 104 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 4x upscaling | MAN | SSIM | 0.7934 | #25 of 104 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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