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Models/RDLUF

RDLUF

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision4 results

Image RestorationonKAIST
PSNR· 2022-11-13
39.57
best: 40.69 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
Image RestorationonKAIST
SSIM· 2022-11-13
0.974
best: 0.978 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
Image RestorationonCAVE
PSNR· 2022-11-13
39.57
best: 40.69 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
Image RestorationonCAVE
SSIM· 2022-11-13
0.974
best: 0.978 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891

Audio4 results

10-shot image generationonKAIST
PSNR· 2022-11-13
39.57
best: 40.69 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
10-shot image generationonKAIST
SSIM· 2022-11-13
0.974
best: 0.978 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
10-shot image generationonCAVE
PSNR· 2022-11-13
39.57
best: 40.69 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891
10-shot image generationonCAVE
SSIM· 2022-11-13
0.974
best: 0.978 (SSR)
SOTA
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging arXiv:2211.06891