Re: Scientifically Based Presharpening for Enlargement

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1. If the main point of the page is implementing a method from a paper,
include a one-paragraph summary of that method in your web page. I'll
use that summary to decide whether I want to read the full paper, and it
will also serve as a substitute if the site with the full paper is
temporarily down."
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I said "loosely based." In fact, the paper has an error that I corrected
and my derivation is very different.

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2. As someone else pointed out, your examples are horrible. The
discussion talks about how images from a sensor are not point sampled,
they are effectively convolved with a small box (the area of a sensel)
before being measured. So show us an example of an image that is either
*direct from a sensor, with no processing applied*, or at least a
simulation of such an image. (For example, you could take a much higher
resolution image and convolve/downsample it to get your example).
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Why do you assume it is not such a simulation? In fact it is.

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What you have on the page at the moment shows really horrendous sharpening
artifacts (halos) as well as compression artifacts that completely
overwhelm the small difference between the two cases that you are trying
to show. Your starting images should be uncompressed and unsharpened.
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There are no compression artifacts in the input images because PNG uses
lossless compression. The differences are obvious to my eyes. If you
don't see the differences you don't need presharpening.

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Also, if the "data dependent Lanczos" filter is what's responsible for the
weird wave-like textures in the road surface at the bottom of the
image, don't use this filter! Adding artifacts that weren't there in the
original is generally a bad idea, and especially bad in something
that's supposed to be an example. Why not use bicubic polynomial
interpolation, which (a) is familiar to many people, and (b) doesn't
create such artifacts. Yes, it doesn't preserve high frequencies as well
as Lanczos, but you should still be able to see the difference
between the images with and without presharpening applied, and that's the
point of the example.
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a. I provided the input images so people could test them with their
favorite enlargement method.

b. That "weird wave-like texture" is less offensive to the eyes than the
jagged edges you will get with bicubic and the halos will be similar with
both. Providing an exotic example without jagged edges illustrates the
versatility of the method.

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3. You might address why the sharpening should be a preprocessing step. If
the sharpening is a linear filter, and interpolation is a linear
filter (and most popular interpolation methods are linear) then it should
not matter what order you perform them in - as long as you deal
with the change in magnification for the sharpening step. You might even
find a way to merge the sharpening into the interpolation.
On the other hand, if the interpolation filter is actually nonlinear, then
that's a reason why the sharpening needs to be a preprocess.
Dave
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Although convolution is linear and commutative, linear operations are not
generally commutative. And, my Data Dependent Lanczos enlargement method
is not linear. An advantage of presharpening too obvious to mention is
that a smaller image can be sharpened more quickly than after enlargement.




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