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Science Forum Index » Image Processing Forum » reconstructing low res image when have some high res...
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| Memo... |
 Posted: Wed Jun 11, 2008 3:23 pm |
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I have a low resolution image and I have high resolution images of
some parts of that low resolution image. What is the best way to
combine that information so I can improve the resolution in areas of
the image where I don't have the high resolution information? |
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| aruzinsky... |
| Posted: Thu Jun 12, 2008 5:15 am |
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On Jun 11, 7:23 pm, Memo <gamali... at (no spam) hotmail.com> wrote:
Quote: I have a low resolution image and I have high resolution images of
some parts of that low resolution image. What is the best way to
combine that information so I can improve the resolution in areas of
the image where I don't have the high resolution information?
Why isn't it obvious to you? You enlarge the low resolution image to
match the high resolution images and seamlessly clone the high
resolution parts into the enlargement. I have done this with a wide
angle picture of a cemetery, cloning a closeup of the face of a
tombstone so that the inscription can be read. |
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| Memo... |
| Posted: Thu Jun 12, 2008 2:06 pm |
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Guest
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On Jun 12, 11:15 am, aruzinsky <aruzin... at (no spam) general-cathexis.com> wrote:
Quote: On Jun 11, 7:23 pm, Memo <gamali... at (no spam) hotmail.com> wrote:
I have a low resolution image and I have high resolution images of
some parts of that low resolution image. What is the best way to
combine that information so I can improve the resolution in areas of
the image where I don't have the high resolution information?
Why isn't it obvious to you? You enlarge the low resolution image to
match the high resolution images and seamlessly clone the high
resolution parts into the enlargement. I have done this with a wide
angle picture of a cemetery, cloning a closeup of the face of a
tombstone so that the inscription can be read.
I think there is a way to get higher resolution in the parts of the
image for which you don't have the high resolution parts if you have
some of the high resolution parts. I think you can do more than just
paste in high res parts. Consider if you only had one line instead of
an image with some missing points but in other parts there were extra
points or frequencies. You could set up a fourier transform matrix
such as
f(t)eiKw1t1 + f(t)eiKw1t2 + ... f(t)eiKw1tn = f(w1)
f(t)eiKw2t1 + f(t)eiKw2t2 + ... f(t)eiKw2tn = f(w2)
.
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f(t)eiKwmt1 + f(t)eiKwmt2 + ... f(t)eiKwmtn = f(wm)
lets say you want a fourier transform of 256 points and you do have
256 points of f(w) but they're not evenly spaced. You could fit the
matrix on the left to those points and solve for f(t) and then try and
get the missing f(ws) from them.
Another approach is to use a sum of integrals of f(t) where each
integral is over a deltat centered at f(t) and you assume that over
deltaT f(t) is a constant value. The smoother f(w) is the better that
works.
I just wonder if there are other approaches. There are problems with
both the above approaches. |
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| aruzinsky... |
| Posted: Thu Jun 12, 2008 5:40 pm |
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On Jun 12, 6:06 pm, Memo <gamali... at (no spam) hotmail.com> wrote:
Quote: On Jun 12, 11:15 am, aruzinsky <aruzin... at (no spam) general-cathexis.com> wrote:
On Jun 11, 7:23 pm, Memo <gamali... at (no spam) hotmail.com> wrote:
I have a low resolution image and I have high resolution images of
some parts of that low resolution image. What is the best way to
combine that information so I can improve the resolution in areas of
the image where I don't have the high resolution information?
Why isn't it obvious to you? You enlarge the low resolution image to
match the high resolution images and seamlessly clone the high
resolution parts into the enlargement. I have done this with a wide
angle picture of a cemetery, cloning a closeup of the face of a
tombstone so that the inscription can be read.
I think there is a way to get higher resolution in the parts of the
image for which you don't have the high resolution parts if you have
some of the high resolution parts. I think you can do more than just
paste in high res parts. Consider if you only had one line instead of
an image with some missing points but in other parts there were extra
points or frequencies. You could set up a fourier transform matrix
such as
f(t)eiKw1t1 + f(t)eiKw1t2 + ... f(t)eiKw1tn = f(w1)
f(t)eiKw2t1 + f(t)eiKw2t2 + ... f(t)eiKw2tn = f(w2)
.
.
f(t)eiKwmt1 + f(t)eiKwmt2 + ... f(t)eiKwmtn = f(wm)
lets say you want a fourier transform of 256 points and you do have
256 points of f(w) but they're not evenly spaced. You could fit the
matrix on the left to those points and solve for f(t) and then try and
get the missing f(ws) from them.
Another approach is to use a sum of integrals of f(t) where each
integral is over a deltat centered at f(t) and you assume that over
deltaT f(t) is a constant value. The smoother f(w) is the better that
works.
I just wonder if there are other approaches. There are problems with
both the above approaches.
You have probably heard of fractal enlargement, e.g., Genuine
Fractals. Google,
"Iterated Function Systems" "image enlargement"
Roughly speaking, "range blocks" are matched approximately to
reductions of larger size "domain blocks." In an iterative process,
an enlarged image is reconstructed from pieces of itself; the range
blocks of one iteration are formed from the domain blocks of the
previous iteration.
Now comes my idea. Paste in high resolution parts after each
iteration. I expect that will improve the image quality in other
areas of the IFS enlargement, but I have no idea how much. Maybe, it
won't even converge.
(Don't anyone dare try to patent this idea after this post because, if
not previously patented or patent pending, this idea is hereby put
into the public domain.) |
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| Martin Brown... |
| Posted: Fri Jun 13, 2008 5:14 am |
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Guest
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Memo wrote:
Quote: I have a low resolution image and I have high resolution images of
some parts of that low resolution image. What is the best way to
combine that information so I can improve the resolution in areas of
the image where I don't have the high resolution information?
I doubt that you can beat the Bayesian approach if computational cost is
not a barrier. Essentially you need to be able to compute the observed
data from the high resolution model image, and also the matrix transpose
of this operation. Armed with these two transforms you can use various
maximum entropy or other constrained non-linear least square methods to
find a model image consistent with the observed data to within the noise.
I suspect that you will not get much more than a slight bleeding of the
high resolution data decaying exponentially away from edges of the high
res areas (with a rate depending on the half width of the effective psf).
Regards,
Martin Brown
** Posted from http://www.teranews.com ** |
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