Jpeg Artifact Correction On Live1 Quality 10 1
Métriques
PSNR
SSIM
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | PSNR | SSIM |
|---|---|---|
| residual-dense-network-for-image-restoration | 29.7 | 0.8252 |
| hierarchical-information-flow-for-generalized | 29.94 | 0.8359 |
| beyond-a-gaussian-denoiser-residual-learning | 29.19 | - |
| image-restoration-using-convolutional-auto | 29.35 | - |
| one-size-fits-all-hypernetwork-for-tunable | 28.81 | 0.82 |
| memnet-a-persistent-memory-network-for-image | 29.45 | 0.8327 |
| quantization-guided-jpeg-artifact-correction | 29.53 | 0.840 |
| multi-level-wavelet-cnn-for-image-restoration | 29.69 | 0.8357 |
| compression-artifacts-reduction-by-a-deep | 29.11 | 0.8235 |
| towards-flexible-blind-jpeg-artifacts-removal | 29.75 | 0.827 |
| dpw-sdnet-dual-pixel-wavelet-domain-deep-cnns | 29.40 | 0.8235 |
| implicit-dual-domain-convolutional-network | 29.71 | 0.838 |
| s-net-a-scalable-convolutional-neural-network | 29.44 | 0.8325 |