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We can conclude that our MLP is a successful approach of inpainting the boundary frame, in addition to recover the FOV without any boundary artifact.
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Again, no noticeable boundary artifacts are shown in Fig. 8.
To reduce boundary artifacts in image deblurring, many methods have been proposed.
These fast transform-based direct deblurring methods, however, often present severe boundary artifacts.
In fact, the Gaussian deblurring also generated boundary artifacts for both rectangular and diagonal blocks.
Block artifacts in Fig. 16b were caused by boundary artifacts generated in diagonal deblurrings of rectangular blocks.
It is also true that boundary artifacts are one of key obstacles in many image deblurring problems [22].
The problem encountered in this method is that the inverse normalization of the embedding regions may result in boundary artifacts.
This approach can reduce boundary artifacts in some degree, but, at the same time, makes it more difficult to recover near boundary image pixels.
In diagonal deblurrings of rectangular blocks, however, boundary artifacts appeared in the final image (Fig. 16b) because of the exact opposite reason.
We can improve significantly the results and avoid annoying boundary artifacts by allowing for a certain amount of overlapping of adjacent regions.
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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