On Jun 12, 11:15=A0am, aruzinsky <aruzin...@[EMAIL PROTECTED]
> wrote:
> On Jun 11, 7:23=A0pm, Memo <gamali...@[EMAIL PROTECTED]
> wrote:
>
> > =A0I have a low resolution image and I have high resolution images of
> > some parts of that low resolution image. =A0What 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? =A0You enlarge the low resolution image to
> match the high resolution images and seamlessly clone the high
> resolution parts into the enlargement. =A0I 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 =3D f(w1)
f(t)eiKw2t1 + f(t)eiKw2t2 + ... f(t)eiKw2tn =3D f(w2)
=2E
=2E
f(t)eiKwmt1 + f(t)eiKwmt2 + ... f(t)eiKwmtn =3D 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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