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Table 3 Quantitative comparison in terms of RMSE, PSNR and SSIM

From: Super-resolution via a fast deconvolution with kernel estimation

Example(factor) Index Bicubic 08’TOG [22] 11’IPOL [7] 11’SIAM [8] 14’TIP [9] Ours
House(2) RMSE 6.4742 6.0985 5.4645 5.3947 7.4008 5.1384
  PSNR 31.9071 32.4263 33.3798 33.4915 30.7452 33.9143
  SSIM 0.8831 0.8858 0.8961 0.8969 0.8761 0.8958
House(3) RMSE 8.9995 7.2818 7.8816 7.8106 9.1071 7.1034
  PSNR 29.0465 30.886 30.1985 30.2771 28.9432 31.1015
  SSIM 0.847 0.8655 0.861 0.8615 0.8554 0.872
House(4) RMSE 11.018 9.1549 9.8511 9.7658 11.7308 9.0703
  PSNR 27.2887 28.8977 28.2611 28.3366 26.7443 28.9784
  SSIM 0.8169 0.8418 0.8302 0.8311 0.8278 0.8444
Race(2) RMSE 11.18 11.2381 10.3093 10.1244 12.5453 10.8124
  PSNR 27.1619 27.1169 27.8662 28.0234 26.1612 27.4524
  SSIM 0.6912 0.6911 0.7353 0.7386 0.6536 0.692
Race(3) RMSE 13.9661 13.0151 13.0624 12.9765 14.1891 13.1136
  PSNR 25.2293 25.8418 25.8103 25.8676 25.0917 25.7764
  SSIM 0.6042 0.6078 0.6407 0.6429 0.5955 0.6171
Race(4) RMSE 15.7714 14.6136 14.8819 14.8098 15.7557 14.6909
  PSNR 24.1734 24.8357 24.6776 24.7198 24.182 24.7898
  SSIM 0.5524 0.562 0.5804 0.5826 0.5575 0.5761