My understanding is based on simplified linear systems theory. Perhaps the reason for not doing deconvolution early on is that it is (usually) nonlinear?
In actual practice there isn't something like a brick-wall filter, it would also introduce ringing. We're forced to seek a compromise.
Sure, but it the lense/OLPF/... introduce significant loss of signal in the passband, it seems sensible to fix it "close to the problem". I meant brick-wall in the loosest possible form, and should probably have said "some nice lowpass shape with passband < fs/2"
I do believe that brick-wall-filtering is _possible_ for limited-duration signals using DFTs? A finite-length sequence contains a finite amount of samples and can be fully described by a finite set of frequency coefficients. Set the desired gain in the DFT-domain, and you have (if desired) brick-wall filtering. The same caveats about frequency-domain vs spatial-domain design trade-offs, though.
As I have been saying, a Lanczos windowed Sinc filter is close to optimal.
This is common knowledge, but what are the conditions for this optimality? Is it only "looks good to me"? What is implied about the source image (gamma?) Or can one insert subjectively motivated constraints into something like the remez algorithm and see the lanczos popping out?
Yes, but be careful because you'll (re)introduce stairstepping (=aliasing artifacts).
According to some photographers, aliasing is much preferred over Nyquistian sampling. I can see why this can be true if you have complete control over the image chain and are willing to fiddle with pixels until it looks "good".
-h
