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Convolution Kernel [message #73813]

Thu, 02 December 2010 09:35

Gray
Messages: 253
Registered: February 2010

Senior Member

Hi all,

Maybe my calculus is screwy, but this doesn't make sense to me.
Here's my issue:

I have two astronomical images (of stars). I've fit an average PSF as
a Moffat profile for each of the two images. I want to find the
optimal convolution kernel to match the two psfs, so I call on my old
friend Mr. Fourier. If MA is the Moffat profile for image A and MB is
the Moffat profile for image B (both 2d), and K is my optimal kernel,
then I can do this:

MA ** K = MB --> ** is convolution in this scenario
F(MA**K) = F(MB) --> F() is the Fourier transform
F(MA) * F(K) = F(MB)
K = F^-1(F(MB)/F(MA))

With me so far? So I do this in IDL.
IDL> ma = moffat(params_a)
IDL> mb = moffat(params_b)
IDL> fma = fft(ma) & fmb = fft(mb)
IDL> k = fft(fma/fmb,/inverse)
IDL> mc = convol(ma,k)

What I get, however, is that MC is a 2d delta function. Why...? It
happens with 2d Gaussians, as well. Thanks for your help!

--Gray

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[Message index]

		Convolution Kernel By: Gray on Thu, 02 December 2010 09:35
		Re: Convolution Kernel By: Jeremy Bailin on Fri, 03 December 2010 10:06
		Re: Convolution Kernel By: Bringfried Stecklum on Fri, 03 December 2010 02:57
		Re: Convolution Kernel By: MC on Fri, 03 December 2010 02:23

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