On Sun, 22 Jun 2003 13:17:49 -0700
Joel Rodriguez [EMAIL PROTECTED] wrote:
In practice this means, that I can have two completely different
images, one that shows a normal image and another one that
completely looks like random noise. But when I use a convolution
on both images the result can
Hi Ernst
Yes, did studied Fourier analysis, do agree with you in almost every thing you
wrote, there are mainly four concepts involved into the discussion, Fourier Analysis, signal,
noise and some sort of an inversion technique, under this point of view everything
sounds just right, but,
Thanks for your attention to the matter Esnst:
it is enough information to get me going :)
I will take a close look at the link which by the way looks very impressive,
the approach was thinking was not conjugate gradient itself, but a variant
of constrained optimization:
On Fri, 20 Jun 2003 23:58:03 -0700
Joel Rodriguez [EMAIL PROTECTED] wrote:
Thanks for your attention to the matter Esnst:
it is enough information to get me going :)
I will take a close look at the link which by the way looks very impressive,
the approach was thinking was not conjugate
Actually I would like to colaborate with a new filter also
(as Bowie), but mine idea is in the direction of:
``Inverse Image Filtering with Conjugate Gradient''
http://people.cornell.edu/pages/zz25/imgcg/
it is a new idea under Gimp? Downloading the latest 1.x
versi'on will look into the
