Decomposing a distribution into Gaussians is the essence of unblurring.  A
good algorithm for doing that would of course be very useful, but there is
an enormous literature on the topic, and the most important fact about it
is that it is mathematically ill-posed.  In other words, unless you add
extra constraints, tiny changes in the source distribution result in very
large changes in the output.  (In image-processing terms, transforms of
that type tend to create large artifacts.) Unless the new method has some
way of handling that problem, it probably isn't going to be useful.

Bill


On Fri, Feb 27, 2015 at 10:29 PM, Nathan Summers <[email protected]>
wrote:

> The first thing that comes to mind is that it might be a good method for
> resizing images.
>
> Rockwalrus
>
> On Fri, Feb 27, 2015, 4:10 PM Ben Thurston <[email protected]>
> wrote:
>
> > Well I guess I should add that it also interpolates the known points of a
> > distribution...
> >
> > On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <[email protected]
> >
> > wrote:
> >
> > > Sorry of course you need the link:
> > >
> > > http://benpaulthurstonblog.blogspot.com/2015/02/
> > supposing-you-have-process-that-reaches.html
> > >
> > > On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <
> [email protected]
> > >
> > > wrote:
> > >
> > >> I developed this type of function that I feel is sort of like the
> > >> statistical analogue of the Fourrier series, it breaks a distribution
> up
> > >> into simple normal distribution components as the Fourrier series
> > breaks a
> > >> wave into simple sine wave components. I thought maybe there could be
> an
> > >> application for it in image processing but I don't know enough about
> > image
> > >> processing to figure out how it would apply... Anyone have any ideas?
> > >>
> > >
> > >
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