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Models/SRMDNF

SRMDNF

Reported on 24 benchmarks across 4 tasks · 1 paper

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

Computer Vision12 results

Image Super-ResolutiononSet14 - 4x upscaling
PSNR· 2017-12-17
28.35
best: 29.54 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Image Super-ResolutiononSet14 - 4x upscaling
SSIM· 2017-12-17
0.777
best: 0.894 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Image Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2017-12-17
25.68
best: 28.72 (Hi-IR-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Image Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2017-12-17
0.773
best: 0.9481 (SPSR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Image Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2017-12-17
27.49
best: 28.16 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Image Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2017-12-17
0.734
best: 0.851 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononSet14 - 4x upscaling
PSNR· 2017-12-17
28.35
best: 29.54 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononSet14 - 4x upscaling
SSIM· 2017-12-17
0.777
best: 0.894 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2017-12-17
25.68
best: 28.72 (Hi-IR-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2017-12-17
0.773
best: 0.9481 (SPSR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2017-12-17
27.49
best: 28.16 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
3D Object Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2017-12-17
0.734
best: 0.851 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116

Graphs6 results

Super-ResolutiononSet14 - 4x upscaling
PSNR· 2017-12-17
28.35
best: 29.54 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Super-ResolutiononSet14 - 4x upscaling
SSIM· 2017-12-17
0.777
best: 0.894 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Super-ResolutiononUrban100 - 4x upscaling
PSNR· 2017-12-17
25.68
best: 28.72 (Hi-IR-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Super-ResolutiononUrban100 - 4x upscaling
SSIM· 2017-12-17
0.773
best: 0.9481 (SPSR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Super-ResolutiononBSD100 - 4x upscaling
PSNR· 2017-12-17
27.49
best: 28.16 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
Super-ResolutiononBSD100 - 4x upscaling
SSIM· 2017-12-17
0.734
best: 0.851 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116

Methodology6 results

16konSet14 - 4x upscaling
PSNR· 2017-12-17
28.35
best: 29.54 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
16konSet14 - 4x upscaling
SSIM· 2017-12-17
0.777
best: 0.894 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
16konUrban100 - 4x upscaling
PSNR· 2017-12-17
25.68
best: 28.72 (Hi-IR-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
16konUrban100 - 4x upscaling
SSIM· 2017-12-17
0.773
best: 0.9481 (SPSR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
16konBSD100 - 4x upscaling
PSNR· 2017-12-17
27.49
best: 28.16 (DRCT-L)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116
16konBSD100 - 4x upscaling
SSIM· 2017-12-17
0.734
best: 0.851 (Edge-informed SR)
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations arXiv:1712.06116