Image Super Resolution On Set5 2X Upscaling
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 |
|---|---|---|
| image-restoration-using-convolutional-auto | 37.66 | 0.9599 |
| fast-and-accurate-image-super-resolution-by | 37.13 | .9569 |
| accurate-image-super-resolution-using-very | 37.53 | - |
| drct-saving-image-super-resolution-away-from | 39.14 | 0.9658 |
| activating-more-pixels-in-image-super | 38.73 | 0.9637 |
| hierarchical-back-projection-network-for | 38.13 | 0.961 |
| channel-partitioned-windowed-attention-and | 38.68 | 0.9633 |
| beyond-a-gaussian-denoiser-residual-learning | 37.58 | - |
| ml-craist-multi-scale-low-high-frequency | 38.19 | 0.9617 |
| neural-nearest-neighbors-networks | 37.57 | - |
| feature-based-adaptive-contrastive | 38.242 | - |
| deep-back-projection-networks-for-single | 38.08 | 0.96 |
| mair-a-locality-and-continuity-preserving | 38.62 | 0.963 |
| progressive-multi-scale-residual-network-for | 38.22 | 0.9612 |
| a-framework-for-real-time-object-detection | 38.21 | 0.9614 |
| lightweight-image-super-resolution-with-1 | 38.00 | - |
| channel-partitioned-windowed-attention-and | 38.72 | 0.9635 |
| drct-saving-image-super-resolution-away-from | 38.72 | 0.9646 |
| learning-deep-cnn-denoiser-prior-for-image | 35.05 | - |
| one-size-fits-all-hypernetwork-for-tunable | 36.69 | 0.94 |
| activating-more-pixels-in-image-super | 38.91 | 0.9646 |
| cascade-convolutional-neural-network-for | 37.45 | 0.9570 |
| fast-accurate-and-lightweight-super | 37.82 | - |
| hmanet-hybrid-multi-axis-aggregation-network | 38.95 | 0.9649 |
| swinfir-revisiting-the-swinir-with-fast | 38.65 | 0.9633 |
| fast-accurate-and-lightweight-super-1 | 37.76 | - |
| image-restoration-using-deep-regulated | 37.42 | - |
| lightweight-feature-fusion-network-for-single | 37.66 | 0.9585 |
| deeply-recursive-convolutional-network-for | 37.63 | - |
| on-efficient-transformer-and-image-pre | 38.63 | 0.9632 |
| densely-residual-laplacian-super-resolution | 38.34 | 0.9619 |
| swinfir-revisiting-the-swinir-with-fast | 38.74 | - |
| mair-a-locality-and-continuity-preserving | 38.56 | 0.9628 |
| local-texture-estimator-for-implicit | 38.33 | - |
| feedback-network-for-image-super-resolution | 38.11 | - |
| sub-pixel-back-projection-network-for | 38.05 | 0.9606 |
| image-super-resolution-with-cross-scale-non | 38.28 | 0.9616 |
| single-image-super-resolution-via-a-holistic | 38.33 | 0.9299 |
| ml-craist-multi-scale-low-high-frequency | 38.15 | 0.9615 |
| multi-level-wavelet-cnn-for-image-restoration | 37.91 | - |
| hierarchical-information-flow-for-generalized | 38.87 | 0.9663 |