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Table 2 Comparisons of peak signal-to-noise ratio(PSNR) values, structural similarity (SSIM) values, and feature similarity (FSIM) values for image with different noise level with different super-resolution approaches

From: Group-based single image super-resolution with online dictionary learning

Noise level   Bi-cubic Yang et al. [20] Glasner et al. [15] ASDS [13] Proposed
σ ν =0 PSNR 28.93 31.21 30.19 3 4.0 5 32.92
  SSIM 0.911 0.926 0.939 0.945 0.9 4 9
  FSIM 0.917 0.948 0.938 0.9 5 9 0.957
σ ν =1 PSNR 28.92 31.13 30.17 31.34 3 2.9 2
  SSIM 0.910 0.918 0.935 0.908 0.9 4 7
  FSIM 0.917 0.945 0.937 0.934 0.9 5 6
σ ν =3 PSNR 28.83 30.49 29.94 31.24 3 2.6 7
  SSIM 0.897 0.863 0.908 0.907 0.9 3 4
  FSIM 0.913 0.918 0.925 0.936 0.9 5 2
σ ν =5 PSNR 28.66 29.41 29.46 30.96 3 2.1 8
  SSIM 0.875 0.779 0.860 0.896 0.9 1 1
  FSIM 0.904 0.872 0.901 0.934 0.9 4 1
  1. The data in italics indicates the best PSNR/SSIM/FSIM result