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developing the algorithm must have been interesting but, really, is that
a 'problem' ?
Il giorno mer, 24/10/2012 alle 00.41 +1300, IVP ha scritto:
He has fully disclosed the process and appears to have made it public domain.
If blurred picture = problem
then yuzhikov(problem) = problem/some number.
I think he did a good job of solving a problem. There are probably other solutions but this one is now available to us all!
On 10/23/2012 9:19 AM, Rossano Gobbi wrote:
> developing the algorithm must have been interesting but, really, is that
> a 'problem' ?
> Il giorno mer, 24/10/2012 alle 00.41 +1300, IVP ha scritto:
> John Ferrell W8CCW
> “During times of universal deceit,
> Telling the TRUTH becomes a revolutionary act”
> George Orwell
Hmmm, it seems like he took some standard algorithms for deconvolution
and made them into an application. Useful but not earth-shattering
(although I have not looked at the source code).
Depending on what kernel an image is convolved with, the inverse
(deconvolution) may or may not be possible without loss of
information. Even if the convolution by a particular kernel is
invertible, it will usually be close to being singular
(non-invertible) and therefore it will amplify noise in the image.
This process is analogous to trying to undo low-pass filtering by
amplifying and high-pass filtering. Any noise which is added
in-between the low-pass filtering (which you cannot control since it
is caused by the out-of-focus condition or the hand motion in this
case) and the post-processing, will be amplified by the process. This
goes for quantization and compression effects, too.
If you already have some additional information about the image, you
can make use of that to get a better de-convolved image, but only if
you didn't already use that information in compressing the image
(i.e., this method is likely to work much better on, say, raw or
losslessly-compressed images than on JPG).
On Tue, Oct 23, 2012 at 7:41 AM, IVP <clear.net.nz> wrote: joecolquitt
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