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While researching images for this week's fiction story, "Agreeable," by Jonathan Franzen, I considered images from Raimond Wouda's body of work "School".
Nevertheless, not only were considered images from this selected group.
Table 4 provides the PSNR values generated by all algorithms on the considered images.
Mei and Rives only considered images wherein the grid points were successfully extracted, which increases noise and rounding errors.
The embedding capacities practically achievable for the considered images are shown in Table 4, for different values of P and using C=3.
Therefore, a perfect cohesion indicates that all considered images refer to each other at the first positions of their ranked lists [9].
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Then, a classification process is performed to provide the final quality class of the considered image.
The considered image edge should belong to an image region showing low gradient magnitudes.
Our interactive code imr allows a user to choose the regions on a considered image where glitches detected by imgclean as CR signatures are ignored.
When the considered image rotates to τ = l 2π/K radians (l = 0, 1, 2,...) then its corresponding feature vector shifts circularly.
Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality.
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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