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Models/SRResNet

SRResNet

Reported on 35 benchmarks across 5 tasks · 2 papers · 18 SOTA

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

Computer Vision16 results

Image Super-ResolutiononSet14 - 4x upscaling
PSNR· 2016-09-15
28.49
best: 29.54 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononSet14 - 4x upscaling
SSIM· 2016-09-15
0.8184
best: 0.894 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-09-15
27.58
best: 28.16 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-09-15
0.762
best: 0.851 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononSet14 - 4x upscaling
PSNR· 2016-09-15
28.49
best: 29.54 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononSet14 - 4x upscaling
SSIM· 2016-09-15
0.8184
best: 0.894 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-09-15
27.58
best: 28.16 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-09-15
0.762
best: 0.851 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononSet14 - 4x upscaling
MOS· 2016-09-15
2.98
best: 3.72 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononBSD100 - 4x upscaling
MOS· 2016-09-15
2.29
best: 3.56 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononSet14 - 4x upscaling
MOS· 2016-09-15
2.98
best: 3.72 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
3D Object Super-ResolutiononBSD100 - 4x upscaling
MOS· 2016-09-15
2.29
best: 3.56 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Image Super-ResolutiononSen2venus - 2x upscaling
PSNR
38.45
best: 49.4784 (Swin2SR-MoSE)
Image Super-ResolutiononSen2venus - 2x upscaling
SSIM
0.96
best: 0.9948 (Swin2SR-MoSE)
3D Object Super-ResolutiononSen2venus - 2x upscaling
PSNR
38.45
best: 49.4784 (Swin2SR-MoSE)
3D Object Super-ResolutiononSen2venus - 2x upscaling
SSIM
0.96
best: 0.9948 (Swin2SR-MoSE)

Graphs8 results

Super-ResolutiononSet14 - 4x upscaling
PSNR· 2016-09-15
28.49
best: 29.54 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononSet14 - 4x upscaling
SSIM· 2016-09-15
0.8184
best: 0.894 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2016-09-15
27.58
best: 28.16 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2016-09-15
0.762
best: 0.851 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononSet14 - 4x upscaling
MOS· 2016-09-15
2.98
best: 3.72 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononBSD100 - 4x upscaling
MOS· 2016-09-15
2.29
best: 3.56 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
Super-ResolutiononSen2venus - 2x upscaling
PSNR
38.45
best: 49.4784 (Swin2SR-MoSE)
Super-ResolutiononSen2venus - 2x upscaling
SSIM
0.96
best: 0.9948 (Swin2SR-MoSE)

Methodology8 results

16konSet14 - 4x upscaling
PSNR· 2016-09-15
28.49
best: 29.54 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konSet14 - 4x upscaling
SSIM· 2016-09-15
0.8184
best: 0.894 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konBSD100 - 4x upscaling
PSNR· 2016-09-15
27.58
best: 28.16 (DRCT-L)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konBSD100 - 4x upscaling
SSIM· 2016-09-15
0.762
best: 0.851 (Edge-informed SR)
SOTA
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konSet14 - 4x upscaling
MOS· 2016-09-15
2.98
best: 3.72 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konBSD100 - 4x upscaling
MOS· 2016-09-15
2.29
best: 3.56 (SRGAN)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
16konSen2venus - 2x upscaling
PSNR
38.45
best: 49.4784 (Swin2SR-MoSE)
16konSen2venus - 2x upscaling
SSIM
0.96
best: 0.9948 (Swin2SR-MoSE)

Miscellaneous3 results

Fine-Grained Urban Flow InferenceonTaxiBJ-P1
MAE· 2019-02-06
2.457
best: 2.011 (UrbanFM)
SOTA
UrbanFM: Inferring Fine-Grained Urban Flows arXiv:1902.05377
Fine-Grained Urban Flow InferenceonTaxiBJ-P1
MSE· 2019-02-06
17.3388
best: 14.9232 (STCF)
SOTA
UrbanFM: Inferring Fine-Grained Urban Flows arXiv:1902.05377
Fine-Grained Urban Flow InferenceonTaxiBJ-P1
MAPE· 2019-02-06
0.713
best: 0.732 (ESPCN)
UrbanFM: Inferring Fine-Grained Urban Flows arXiv:1902.05377