Noise Estimation On Sidd
Métriques
Average KL Divergence
PSNR Gap
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Average KL Divergence | PSNR Gap |
|---|---|---|
| grdngrouped-residual-dense-network-for-real | 0.443 | 2.28 |
| toward-convolutional-blind-denoising-of-real | 0.728 | 8.30 |
| learning-to-generate-realistic-noisy-images-2 | 0.153 | 0.84 |
| unprocessing-images-for-learned-raw-denoising | 0.545 | 4.90 |
| dual-adversarial-network-toward-real-world | 0.212 | 2.06 |