Image Super Resolution On Manga109 2X
Metriken
PSNR
SSIM
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | PSNR | SSIM |
|---|---|---|
| ml-craist-multi-scale-low-high-frequency | 39.26 | 0.9786 |
| ml-craist-multi-scale-low-high-frequency | 39.23 | 0.9785 |
| lightweight-feature-fusion-network-for-single | 37.93 | 0.9746 |
| activating-more-pixels-in-image-super | 41.01 | 0.9831 |
| image-super-resolution-with-cross-scale-non | 39.37 | 0.9785 |
| feedback-network-for-image-super-resolution | 39.08 | - |
| progressive-multi-scale-residual-network-for | 39.15 | 0.9781 |
| channel-partitioned-windowed-attention-and | 40.59 | 0.9816 |
| hmanet-hybrid-multi-axis-aggregation-network | 41.13 | 0.9836 |
| drct-saving-image-super-resolution-away-from | 40.41 | 0.9814 |
| drct-saving-image-super-resolution-away-from | 41.14 | 0.9842 |
| densely-residual-laplacian-super-resolution | 39.75 | 0.9792 |
| activating-more-pixels-in-image-super | 40.71 | 0.9819 |
| lightweight-image-super-resolution-with-1 | 38.88 | - |
| channel-partitioned-windowed-attention-and | 40.48 | 0.9814 |
| swinfir-revisiting-the-swinir-with-fast | 40.77 | - |
| hierarchical-back-projection-network-for | 39.3 | 0.979 |
| single-image-super-resolution-via-a-holistic | 39.62 | 0.9787 |
| swinfir-revisiting-the-swinir-with-fast | 40.61 | 0.9816 |
| hierarchical-information-flow-for-generalized | 41.22 | 0.9846 |
| deep-back-projection-networks-for-single | 39.28 | 0.977 |