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Moreover, the presented methodology is very flexible in incorporating image properties in a wide range of applications (such as materials imaging) and can also be utilized for other interference microscopy techniques.
Besides, image properties have also an impact on efficiency of filtering.
Depending on different image properties, different algorithms for measuring the contrast are applied accordingly.
It considers image properties in a more advanced manner than the NR method.
On the one hand, texture descriptors have to capture all intrinsic image properties.
The analogy between the proposed and test-target methods arises from using known image properties.
However, the spares local sharpness analysis does not measure global image properties.
It is noted that different efficiencies of image filtering result from the test image properties.
The quantification of single image properties by objective metrics is not enough to simulate human perception.
These classification is based on both severity for the disease and image properties.
To find out, researchers measured 393 image properties, from the size of the eyes and cheeks to the shape of the chin.
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