Re: Scientifically Based Presharpening for Enlargement

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So if you did something different from the paper, it's even more important
to tell the reader what you did!
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I suspect most proprietary algorithms are "something different from a
paper." Do you understand "proprietry?"

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Then where do the halos around (for example) the light poles come from? A
raw image from a sensor would not have that. The effect of sensels
that are not points is to attenuate some of the high frequency content of
the image - it would not boost any frequencies. Yet the "before"
images show sharpening artifacts.
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Again, you allege something that is completely false. The input image,
without presharpening, clownBox0.25X.png, has absolutely no halos. You
can prove this to yourself by inspecting a nearest neighbor enlargement.
There are halos in the other images from the presharpening and from DDL
and bicubic enlargement.

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I do see differences between the two images, but those differences are
minor compared to the really obvious problems in the images. I'd be
much more interested to see how your method works on high-quality images
without the artifacts. To be blunt, the input images look bad. If they
were mine, I wouldn't show them to anyone. They will look bad after
enlargement as well. That makes it hard to judge the value of the
presharpening.
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Again there are no artifacts in the input image and the simulation closely
matches pictures from a Sigma SD9 digcam with Foveon sensor, except without
noise.

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Bicubic interpolation does not give me "jagged edges". You *can* get
somewhat "braided" looking effects on high-contrast diagonal edges if you
are using an interpolation method that also boosts high frequencies, but
not all bicubic polynomial interpolation methods do that - it depends on
the coefficient of the cubic term. (If I remember correctly,
a coefficient of -0.5 does not boost high frequencies, one of -0.75
does).
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It is well recognized thoughout literature that bicubic produces jagged
edges and halos. I used -0.5 in the examples.

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And that waviness in the road texture looks completely unrealistic. I'd
rather have a uniform blur than that.
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Again, higher PSNR.

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OK. So you should describe it too. The regular Lanczos interpolation
method *is* linear.
So if you did something different from the paper, it's even more important
to tell the reader what you did!
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Most people who care would guess from the title words "Data Dependent"
that the method is locally adaptive and therefore nonlinear. I see no
overwhelming reason to confirm their suspicions.



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