Image Processing Reference
InDepth Information
proposed algorithm can beter recover details, eliminates the “halo efect” and suppress noise.
Moreover, the results of our algorithm look more harmonious and natural.
For a more definite description of the experimental results, this article also uses objective
evaluation criteria to test the effectiveness of our algorithm. We examine our algorithm in
mean value, standard deviation and timeconsuming (Computer configuration: CPU: Penti
um(R) 3.00 GHz; Memory: 3.00 GB; Programming Language: Matlab). The image mean re
lects the brightness level of the image; the standard deviation reflects the image contrast; the
timeconsuming reflects the time complexity of the algorithm. The results are shown in
Tables
1

4
.
Mean
Standard Deviation Time Consuming (s)
Source image
32.290669
37.976650
Michael Elad
74.522833
34.043863
125.203029
MSRCR
122.023486 33.550094
8.073667
proposed algorithm 92.0300
38.6720
1.246996
Mean
Standard Deviation Time Consuming (s)
Source image
19.074728
28.879643
Michael Elad
54.979484
32.582415
174.404951
MSRCR
105.600284 41.911008
14.030690
proposed algorithm 87.0683
46.6404
1.724475
Mean
Standard Deviation Time Consuming (s)
Source image
42.871070
57.089267
Michael Elad
74.523826
51.653054
177.822975
MSRCR
125.407817 52.732457
11.619577
proposed algorithm 81.8586
61.3861
1.39663
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