The Scientific World Journal / 2014 / Article / Tab 5 / Research Article
A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression Table 5 Table results in PSNR and MSSIM for SVM methods. The model used in all cases is with
and
.
represents the mean and
represents the standard deviation obtained using 10 different noise patterns.
(a) PSNR results: Albert Noise % SVM-M1 SVM-M2 SVM-R 10 37.806326 0.081000 37.634073 0.095187 37.806326 0.081000 20 34.012864 0.077311 34.247895 0.100104 34.012864 0.077311 30 31.334875 0.075222 32.111316 0.096603 31.334875 0.075222 40 28.782425 0.080101 30.524735 0.041627 28.782425 0.080101 50 27.047406 0.054255 29.098885 0.054799 27.047406 0.054255 60 24.689836 0.179538 27.809312 0.058029 24.689836 0.179538 70 22.478286 0.124136 26.584962 0.067960 22.478286 0.124136 80 15.891552 0.149735 25.358707 0.047150 15.891552 0.149735 90 8.109299 0.022287 23.885132 0.045141 8.109299 0.022287
(b) PSNR results: Lenna Noise % SVM-M1 SVM-M2 SVM-R 10 40.210190 0.220927 40.647102 0.137564 40.483306 0.123692 20 35.529780 0.412996 36.987532 0.152243 37.014919 0.148138 30 32.294158 0.230317 34.537564 0.092911 34.751509 0.085640 40 28.576325 0.381850 32.600946 0.062708 32.823432 0.055126 50 26.726578 0.183652 30.849589 0.072874 31.000031 0.114136 60 23.778253 0.182517 29.311999 0.071502 29.154773 0.102010 70 20.483630 0.121690 27.827742 0.055517 27.053785 0.074279 80 12.017827 0.171127 26.298444 0.065581 24.520623 0.047364 90 4.453599 0.014529 24.109759 0.068257 20.390460 0.101721
(c) MSSIM results: Albert Noise % SVM-M1 SVM-M2 SVM-R 10 0.970160 0.000260 0.970334 0.000257 0.969802 0.000227 20 0.932638 0.000469 0.936817 0.000381 0.936707 0.000548 30 0.884381 0.000755 0.899063 0.000610 0.900619 0.000888 40 0.821351 0.000890 0.855522 0.000718 0.860015 0.000879 50 0.756570 0.001241 0.804625 0.000673 0.813316 0.000822 60 0.672137 0.002908 0.746005 0.000909 0.758855 0.001158 70 0.579394 0.003105 0.679557 0.000984 0.691942 0.001030 80 0.368391 0.006010 0.604714 0.001191 0.604779 0.001498 90 0.093756 0.001267 0.510287 0.001127 0.476452 0.002035
(d) MSSIM results: Lenna Noise % SVM-M1 SVM-M2 SVM-R 10 0.985231 0.000228 0.985689 0.000188 0.985842 0.000181 20 0.964831 0.000407 0.969028 0.000366 0.969652 0.000358 30 0.936021 0.000549 0.949325 0.000391 0.950161 0.000462 40 0.891417 0.003147 0.925923 0.000477 0.926024 0.000398 50 0.847043 0.001220 0.896965 0.000344 0.893932 0.000713 60 0.777944 0.002245 0.862579 0.000881 0.850796 0.001099 70 0.679344 0.002105 0.820914 0.000564 0.788319 0.001127 80 0.386960 0.004851 0.768412 0.000831 0.693430 0.001324 90 0.046905 0.000822 0.687110 0.000864 0.526401 0.002583