Image Super Resolution On Urban100 2X
평가 지표
PSNR
SSIM
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | PSNR | SSIM |
|---|---|---|
| a-framework-for-real-time-object-detection | 32.83 | 0.9353 |
| hmanet-hybrid-multi-axis-aggregation-network | 35.24 | 0.9513 |
| sub-pixel-back-projection-network-for | 32.07 | 0.9277 |
| feature-based-adaptive-contrastive | 32.878 | - |
| image-super-resolution-with-cross-scale-non | 33.25 | 0.9386 |
| multi-level-wavelet-cnn-for-image-restoration | 32.3 | - |
| accurate-image-super-resolution-using-very | 30.76 | - |
| activating-more-pixels-in-image-super | 34.81 | 0.9489 |
| drct-saving-image-super-resolution-away-from | 35.17 | 0.9516 |
| deep-back-projection-networks-for-single | 32.92 | 0.935 |
| progressive-multi-scale-residual-network-for | 32.78 | 0.9342 |
| densely-residual-laplacian-super-resolution | 33.54 | 0.9402 |
| activating-more-pixels-in-image-super | 35.09 | 0.9505 |
| ml-craist-multi-scale-low-high-frequency | 32.93 | 0.9361 |
| deeply-recursive-convolutional-network-for | 30.75 | - |
| beyond-a-gaussian-denoiser-residual-learning | 30.74 | - |
| swinfir-revisiting-the-swinir-with-fast | 34.57 | 0.9473 |
| hierarchical-information-flow-for-generalized | 35.16 | 0.9505 |
| ml-craist-multi-scale-low-high-frequency | 33.04 | 0.937 |
| drct-saving-image-super-resolution-away-from | 34.54 | 0.9474 |
| lightweight-image-super-resolution-with-1 | 32.17 | - |
| channel-partitioned-windowed-attention-and | 34.89 | 0.9487 |
| swinfir-revisiting-the-swinir-with-fast | 34.94 | - |
| feedback-network-for-image-super-resolution | 32.62 | - |
| fast-accurate-and-lightweight-super | 31.93 | - |
| local-texture-estimator-for-implicit | 33.5 | - |
| single-image-super-resolution-via-a-holistic | 33.53 | 0.9398 |
| channel-partitioned-windowed-attention-and | 34.76 | 0.9481 |
| hierarchical-back-projection-network-for | 33.12 | 0.938 |