I found an online example, but yet not much detail on the justification.

http://www.imagemagick.org/Usage/fourier/#contrast

Matteo ]]>

I was reading on a book that if you fourier transform an image, separate magnitude and phase, increase or decrease the magnitude by adjusting its exponent, say to 1.2 or 0.98, and recombine with phase through inverse fourier, this amounts to changing the contrast. THe source however was uniquely ppor in details. Would you care to elaborate on why this works?

Thank you

Matteo

]]>Thanx

Martin

]]>All the topics you suggest sound very interesting- I hope you get to cover them all at some point! Although it’s already a pretty big list.

I think I suggested a few blogs ago that I’d be interested in Gibbs phenomenon and ringing (and I guess this fits in nicely with filtering).

Also phase sounds like a good one (as I work a lot with this at the moment)- I’ve seen some cool examples of images reconstructed just from the phase info to demonstrate it’s importance to vision.

And how about aliasing/anti-aliasing images. How does one best pick the right cut-off frequency for low-pass filtering any given aliased image?

Thanks,

Fen

]]>What I find surprising is that this doesn’t seem to work in general. For example when I took a section of linear chirp and MATLAB vector multiplied it by linear chirps of various frequencies and chirp rates (it would be a 2D grid) I got a nice spike if I hit the chirp parameters bang on but no nice ‘near miss’ behaviour to interpolate.

So, what gives? What’s so special in this respect about the DFT? The best explanation I’ve found so far is p164 of Rader and Gold. But any enlightenment you can offer would be great.

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