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Models/SRMD

SRMD

Reported on 110 benchmarks across 11 tasks · 1 paper · 77 SOTA

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

Computer Vision50 results

  • 3D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)
  • VideoonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)
  • Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)
  • Video Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)
  • 3D Object Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)

Knowledge Base20 results

  • 2D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 2D Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)
  • 3D Absolute Human Pose EstimationonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)

Graphs10 results

  • Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • Super-ResolutiononMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)

Methodology10 results

  • 3DonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3DonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)

Playing Games10 results

  • 3D Face AnimationonMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 3D Face AnimationonMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)

Audio10 results

  • 1 Image, 2*2 StitchionMSU Video Upscalers: Quality Enhancement
    LPIPS· 2017-12-17
    0.349
    best: 0.177 (BSRGAN)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Upscalers: Quality Enhancement
    PSNR· 2017-12-17
    30.96
    best: 31.28 (VEAI-AHQ-12)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    ERQAv1.0· 2017-12-17
    0.594
    best: 0.758 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    FPS· 2017-12-17
    5.882
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    PSNR· 2017-12-17
    27.672
    best: 31.669 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    SSIM· 2017-12-17
    0.834
    best: 0.902 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    Subjective score· 2017-12-17
    3.468
    best: 7.628 (VRT)
    SOTA
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Upscalers: Quality Enhancement
    SSIM· 2017-12-17
    0.852
    best: 0.939 (iSeeBetter)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    1 - LPIPS· 2017-12-17
    0.877
    best: 0.623 (DFDnet)
    Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsarXiv:1712.06116
  • 1 Image, 2*2 StitchionMSU Video Super Resolution Benchmark: Detail Restoration
    QRCRv1.0
    0
    best: 0.722 (VRT)