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Models/DeepDeblur-PyTorch

DeepDeblur-PyTorch

Reported on 12 benchmarks across 4 tasks

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

Audio6 results

  • 10-shot image generationonGoPro
    PSNR· uses extra data
    30.12
    best: 35.98 (BSSTNet)
  • 10-shot image generationonGoPro
    Params (M)· uses extra data
    11.72
    best: 80.3 (UFPDeblur)
  • 10-shot image generationonGoPro
    SSIM· uses extra data
    0.9021
    best: 0.9792 (BSSTNet)
  • 1 Image, 2*2 StitchionGoPro
    PSNR· uses extra data
    30.12
    best: 34.6 (AdaRevD)
  • 1 Image, 2*2 StitchionGoPro
    Params (M)· uses extra data
    11.72
    best: 80.3 (UFPDeblur)
  • 1 Image, 2*2 StitchionGoPro
    SSIM· uses extra data
    0.9021
    best: 0.972 (AdaRevD)

Computer Vision3 results

  • Image DeblurringonGoPro
    PSNR· uses extra data
    30.12
    best: 34.6 (AdaRevD)
  • Image DeblurringonGoPro
    Params (M)· uses extra data
    11.72
    best: 80.3 (UFPDeblur)
  • Image DeblurringonGoPro
    SSIM· uses extra data
    0.9021
    best: 0.972 (AdaRevD)

Methodology3 results

  • 16konGoPro
    PSNR· uses extra data
    30.12
    best: 34.6 (AdaRevD)
  • 16konGoPro
    Params (M)· uses extra data
    11.72
    best: 80.3 (UFPDeblur)
  • 16konGoPro
    SSIM· uses extra data
    0.9021
    best: 0.972 (AdaRevD)