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In order to show the effect of our method, some numerical results are presented.
Figure 2 tests the denoising effect of our method on a standard digital image.
In order to show the effect of our method, we give some numerical results in this section.
In the following, we present several experimental results on synthetic and real PolSAR images to show the object segmentation effect of our method.
In order to demonstrate the acceleration effect of our method, experiments of both 3B and FID3B with the grid dataset mentioned above were carried out on a platform with an Intel E7500 (2.93 GHz) CPU.
In addition, we divide our method into three parts to specifically determine the effect of our method: (1) intra-normalization and centroid adaptation, (2) residual normalization and LCS, and (3) SDE.
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Some numerical tests show the practical effects of our method; as well, we compare it with the classical choice for the boundary data in terms of the uniform consistency and the order of uniform convergence of the numerical scheme.
We compare the effects of our method with those of other algorithms and present some discussion in Section 6.
In Section 6, we compare the effects of our method with those of the other algorithms and present some discussion.
Thus, as we explain later in this paper expanding D to D ' has no effect in the outcome of our method.
This target disease is proper and optimal to show the necessity and effects of our methods to converge heterogeneous medical data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com