Image Super Resolution On Manga109 3X
評価指標
PSNR
SSIM
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | PSNR | SSIM |
|---|---|---|
| densely-residual-laplacian-super-resolution | 34.94 | 0.9518 |
| channel-partitioned-windowed-attention-and | 35.66 | 0.9559 |
| ml-craist-multi-scale-low-high-frequency | 34.26 | 0.9492 |
| image-super-resolution-with-cross-scale-non | 34.45 | 0.9502 |
| activating-more-pixels-in-image-super | 35.84 | 0.9567 |
| channel-partitioned-windowed-attention-and | 35.77 | 0.9563 |
| swinfir-revisiting-the-swinir-with-fast | 35.92 | - |
| ml-craist-multi-scale-low-high-frequency | 34.42 | 0.9501 |
| activating-more-pixels-in-image-super | 36.02 | 0.9576 |
| single-image-super-resolution-via-a-holistic | 34.87 | 0.9509 |
| hmanet-hybrid-multi-axis-aggregation-network | 36.10 | 0.9580 |
| progressive-multi-scale-residual-network-for | 34.1 | 0.9480 |
| hierarchical-information-flow-for-generalized | 36.12 | 0.9588 |
| swinfir-revisiting-the-swinir-with-fast | 35.77 | 0.9563 |
| lightweight-image-super-resolution-with-1 | 33.61 | - |
| feedback-network-for-image-super-resolution | 34.18 | - |
| lightweight-feature-fusion-network-for-single | 32.8 | 0.9381 |