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Table 2 Filtering results in MSSIM for images corrupted with mixed impulse noise.

From: Impulse Noise Filtering Using Robust Pixel-Wise S-Estimate of Variance

  Lena Goldhill Boats Bridge
Methods
Noisy 0.115 0.051 0.026 0.132 0.056 0.028 0.145 0.067 0.035 0.226 0.103 0.052
MED3 3 0.880 0.803 0.641 0.796 0.719 0.561 0.856 0.775 0.610 0.689 0.591 0.443
TSM 0.944 0.801 0.491 0.919 0.773 0.477 0.928 0.777 0.478 0.861 0.715 0.456
ACWM 0.958 0.864 0.638 0.926 0.820 0.594 0.946 0.841 0.611 0.870 0.748 0.535
SDROM 0.951 0.830 0.593 0.928 0.801 0.574 0.941 0.811 0.575 0.884 0.755 0.539
PSM 0.737 0.673 0.631 0.772 0.692 0.614 0.768 0.668 0.595 0.832 0.711 0.581
PWMAD 0.958 0.860 0.447 0.923 0.803 0.420 0.942 0.835 0.438 0.868 0.730 0.416
Trilateral 0.942 0.790 0.384 0.919 0.761 0.372 0.931 0.777 0.388 0.870 0.712 0.370
DWM 0.949 0.892 0.776 0.920 0.837 0.701 0.934 0.863 0.734 0.843 0.734 0.583
FRINR 0.948 0.892 0.811 0.916 0.814 0.704 0.943 0.864 0.754 0.874 0.726 0.565
GP 0.960 0.891 0.685 0.930 0.844 0.639 0.949 0.870 0.661 0.872 0.767 0.572
ACWM-EPR 0.959 0.864 0.573 0.933 0.826 0.554 0.949 0.847 0.555 0.859 0.760 0.531
ROLD-EPR 0.947 0.908 0.791 0.932 0.868 0.720 0.941 0.874 0.727 0.896 0.756 0.571
PWS-EPR 0.963 0.903 0.792 0.944 0.860 0.723 0.957 0.876 0.736 0.902 0.777 0.573