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A relation between the variation of contrast at different resolutions and the local Lipschitz regularity of the image is exploited.
In particular, it exploits the link between the change of contrast (as the resolution is increased) and the local Lipschitz regularity of the image [19, 20].
In particular, they propose to exploit the relation between the variation of contrast at different resolutions and the local Lipschitz regularity of the image, in order to classify each pixel as noise pixel, edge pixel, or pixel belonging to a smooth region.
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First, to establish equivalent relationship between Besov space norm F B q β L p Ω and wavelet coefficients norm C j, k i, i = 1, 2, 3. Suppose f is a noise image in the interval Ω = [0, 1 2. It is proper to use Besov space norm to describe the regularity of image [11, 12].
We are interested here in analyzing the link between the pointwise Lipschitz regularity and the variation of contrast of the image.
In the second stage, the link between the local Lipschitz regularity and the change of contrast of the image across scales is exploited; after this stage, adaptive contrast enhancement can be performed on the faded image.
Open image in new window Figure 9 Regularity of the energy consumption of 1000 m diving depth.
Open image in new window Figure 8 Regularity of the energy consumption with the net buoyancy B, diving depth d and the number of profiles n.
But we also notice that in case of the non-neoplastic images ridges destroy the regularity in some parts of the images.
Figure 3 shows significant increases in the regularity of the LFPs, as measured by AC, as a function of stimulus regularity in HG.
Movement professes the regularity of the bar line but violates that regularity at every moment.
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