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Table 6 SSIM comparison for Gaussian blur plus Gaussian noise

From: A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

Image Method SSIM
   Scenario Scenario Scenario Scenario
   I II III IV
Aerial Corrupted 0.8891 0.7794 0.8176 0.7117
491×434 L0-Abs 0.9087 0.8050 0.7821 0.6448
  TVMM 0.9215 0.8325 0.7885 0.6604
  BM3DDEB 0.9198 0.8571 0.8400 0.7710
  IDD-BM3D 0.9292 0.8591 0.8406 0.7577
  AWF 0.9199 0.8519 0.8517 0.7733
  CAWF 0.9273 0.8662 0.8649 0.7957
Bridge Corrupted 0.8836 0.7975 0.7745 0.6929
5122 L0-Abs 0.8902 0.8044 0.7425 0.6524
  TVMM 0.8995 0.8156 0.7323 0.6689
  BM3DDEB 0.9099 0.8579 0.8294 0.7796
  IDD-BM3D 0.9173 0.8573 0.8252 0.7657
  AWF 0.9080 0.8503 0.8344 0.7712
  CAWF 0.9155 0.8637 0.8462 0.7892
River Corrupted 0.8373 0.7198 0.7246 0.6161
(Kodak 11) L0-Abs 0.8328 0.7159 0.6670 0.5560
768×512 TVMM 0.8452 0.7219 0.6344 0.5421
  BM3DDEB 0.8487 0.7713 0.7377 0.6745
  IDD-BM3D 0.8672 0.7767 0.7463 0.6666
  AWF 0.8573 0.7736 0.7676 0.6878
  CAWF 0.8707 0.7949 0.7837 0.7105
Bones Corrupted 0.8231 0.7323 0.6681 0.5884
512×768 L0-Abs 0.8051 0.7287 0.6652 0.6181
  TVMM 0.7954 0.7192 0.6427 0.6172
  BM3DDEB 0.8320 0.7768 0.7420 0.7064
  IDD-BM3D 0.8421 0.7753 0.7389 0.6969
  AWF 0.8358 0.7699 0.7526 0.7000
  CAWF 0.8476 0.7866 0.7615 0.7108
Building Corrupted 0.8119 0.7162 0.6609 0.5753
768×512 L0-Abs 0.8356 0.7491 0.7142 0.6397
  TVMM 0.8454 0.7592 0.6936 0.6615
  BM3DDEB 0.8520 0.7913 0.7633 0.7138
  IDD-BM3D 0.8662 0.7953 0.7715 0.7130
  AWF 0.8509 0.7848 0.7721 0.7147
  CAWF 0.8607 0.7974 0.7816 0.7268
Gazebo Corrupted 0.8279 0.7413 0.6819 0.6030
768×512 L0-Abs 0.8651 0.7867 0.7527 0.6788
  TVMM 0.8741 0.8057 0.7785 0.6826
  BM3DDEB 0.8805 0.8249 0.8001 0.7500
  IDD-BM3D 0.8898 0.8282 0.8064 0.7504
  AWF 0.8796 0.8190 0.8035 0.7481
  CAWF 0.8801 0.8238 0.8072 0.7561