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Conditions for which these requirements are satisfied depend upon a test image and noise parameters.
Figure 4 The test image and results by different algorithms in a simple environment.
We divided the test image and its Gaussian version into sub-images and clustered them using K-means clustering algorithm.
The author in [8] used the difference between test image and its recompressed versions for locating ADJPEG compressed regions.
The original test image and the one corrupted by Gaussian noise are decomposed by SWT and NSST into three levels.
The optimal registration parameter, given by, is one which gives minimum value of between the transformed test image and.
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Fig. 7 Nine test images and a watermark binary image.
Furthermore, we added text regions to these test images and obtained corrupted images.
We have checked other test images and other noise parameter sets.
Comparison results for a set of grayscale test images and several variances of noise are presented.
The experiments were conducted on standard test images and for different concentrations of mixed impulse noise.
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