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From: CCoder on 13 Apr 2010 18:41
Hello,
I am looking for a deconvolution algorithm to detect material transitions.
Can anyone point me in the right direction?



From: Tim Wescott on 13 Apr 2010 20:35
CCoder wrote:
> Hello,
> I am looking for a deconvolution algorithm to detect material transitions.
> Can anyone point me in the right direction?

What do you mean by "material transitions"? What's your source data?
How is it related to these "material transitions" you want to detect?

--
Tim Wescott
Control system and signal processing consulting
www.wescottdesign.com
From: glen herrmannsfeldt on 13 Apr 2010 20:45
CCoder <michel.timos(a)n_o_s_p_a_m.gmail.com> wrote:

> I am looking for a deconvolution algorithm to detect material transitions.
> Can anyone point me in the right direction?

My favorite deconvolution book is "Deconvolution of Images and Spectra"
(I believe that is the title). It is the second edition, the first
had a slightly different title.

That may or may not apply to your problem.

-- glen
From: Rune Allnor on 14 Apr 2010 06:48
On 14 apr, 00:41, "CCoder" <michel.timos(a)n_o_s_p_a_m.gmail.com> wrote:
> Hello,
> I am looking for a deconvolution algorithm to detect material transitions.
> Can anyone point me in the right direction?

Pick any direction you want and walk straight ahead.

Deconvolution is an art. There are no generic methods
that work. The methods that kind of work rely extensively
on very specific properties of the data at hand, and the
measurement set-up that produced them.

Describe what you are up to in some detail, and you might
recieve more useful answers.

Rune
From: glen herrmannsfeldt on 14 Apr 2010 07:42
Rune Allnor <allnor(a)tele.ntnu.no> wrote:
(snip)

> Deconvolution is an art. There are no generic methods
> that work. The methods that kind of work rely extensively
> on very specific properties of the data at hand, and the
> measurement set-up that produced them.

The book I previously mentioned, "Deconvolution of Images and Spectra"
by Jansson, describes non-linear deconvolution especially in the
case where the data values are restricted.

Consider absorption spectra: the absorption can't be less than
zero (well, maybe for fluorescence) or greater than one.
Linear deconvolution of signals with even a little noise will easily
violate those restrictions. Jansson describes algorithms that work
within those limits and, reasonably often, give good results.

There is also an older book: "Deconvolution with applications
in spectroscopy."

-- glen
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