Amplitude compression

Richard Owlett wrote:

> >>HOWEVER it suggests an idea. For an area whose amplitude is less than
> >>some value, give higher gain to a region with a peak whose amplitude is
> >>a absolute amount different that the average of a larger surrounding
> >>area. [Compare to using a magnifying glass to examine the immediate area
> >>of a blip on a topographic map.]
> >
> >
> > That description sounds like an unsharp mask (AKA unsharp filter).
> > Basically you would subtract some weighted amount of the mean in the
> > neighborhood surrounding each point. It's a image processing
> > technique, but what you have is equivalent to a grayscale bitmap. I
> > assume your 3d surface is a uniform 2d grid with each point having its
> > own z height.
> >
> 
> Googled. YES!
> http://tavmjong.free.fr/INKSCAPE/MANUAL/html/Filters-Pixel.html has a
> tantalizing description. The hits are either for canned programs of
> research discussions. Any suggestions for introductory material that's
> not tied to a specific program. Meanwhile I'll wander Scilab toolboxes
> for image manipulation routines.


	As I said I'm assuming your 3d representation is in the form of a  2d
array that contains the height of the 3d surface  at any point. This
is equivalent to a grayscale bitmap. I don't know Scilab but I would
expect it has the tools for doing many of the image processing
functions shown on the website you found.  A grayscale image array
contains values that are integers in the range 0-255, but Scilab may
also have the ability to accept arrays composed of floats with a range
of 0-1.0.

	The unsharp filter is an old photo developer's dark room technique
that consists of subtracting from the image a blurred version of the
original. This is done in a way so that the mean or DC level of the
result remains unchanged. Obviously there are lots of ways to blur an
image so there isn't one single right way to do this. 
	In the page you posted the unsharp filter they show is equivalent to
taking the image with a gain of 18 and subtracting from that a blurred
version that has been blurred by a 3x3 boxcar filter. The subtracted
blurred image has a gain of 9. So subtraction results in an image with
a DC gain of 9 so then they divide by 9 (divisor=9) to produce a
result with a DC gain of one. To produce the same result, those steps
could be done as a series of individual operations or all as one
filter operation.

-jim


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On Aug 18, 4:19 am, Richard Owlett <rowl...@atlascomm.net> wrote:
> ...
> What is "split window"?

A split window averager is a rectangular filter with one or more
center values set to zero so that the bin being  normalized is not
included in the background estimate and so, can not bias the
background estimate.

>
>
> > This assumes that the signals of interest are narrower in spectral
> > width than the background features to be removed. The window might be
> > made the full passband.
>
> I'm not sure if we use "feature" in the same sense.
> For each time slice, I FFT the input.
> A particular bin _may_ be in a feature if above a threshold value.
> To be a feature bin(s) in adjacent slice(s) must also be above
> threshold. [e.g. artic loon's call goes up in frequency with time]
>

By "feature" I mean any signal that you don't want normalized away.

Dale B. Dalrymple
http://dbdimages.com