Jpeg Artifact Correction On Live1 Quality 20 1
Metriken
PSNR
PSNR-B
SSIM
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | PSNR | PSNR-B | SSIM |
|---|---|---|---|
| multi-level-wavelet-cnn-for-image-restoration | 32.04 | 31.83 | 0.8989 |
| image-restoration-using-convolutional-auto | 31.73 | - | - |
| towards-flexible-blind-jpeg-artifacts-removal | 32.13 | 31.57 | 0.889 |
| s-net-a-scalable-convolutional-neural-network | 31.83 | 31.76 | 0.8975 |
| beyond-a-gaussian-denoiser-residual-learning | 31.59 | - | - |
| residual-dense-network-for-image-restoration | 32.1 | - | 0.8886 |
| compression-artifacts-reduction-by-a-deep | 31.29 | 31.37 | 0.8891 |
| memnet-a-persistent-memory-network-for-image | 31.83 | 31.74 | 0.8970 |
| dpw-sdnet-dual-pixel-wavelet-domain-deep-cnns | 31.69 | 31.60 | 0.8891 |
| hierarchical-information-flow-for-generalized | 32.31 | - | 0.8938 |
| implicit-dual-domain-convolutional-network | 32.09 | 32.00 | 0.9006 |
| quantization-guided-jpeg-artifact-correction | 31.86 | 31.27 | 0.901 |