That would be 2 samples for each feature detail you want. Things work OK in the real world at about 4 or 5 samples, but it takes 10 or 12 samples to work pretty well.
a) Real world scenes often go below the Nyquist limit ( e.g pattern from a brick wall at sufficient distance)
b)In a camera without a low-pass filter (e.g. the Ricoh GR) in practice you don't see Moire but you can definitely generate it artificially off real world objects
c) The Point Spread function seems quite important (e.g a 1-pixel scene feature for the GR will have half the effect on the sensor as a 2-pixel function because of the point spread)
d) I'm a bit confused as why Fourier/ DCT all work as well as they do, as the Nyquist assumption for a modern digital camera seems incorrect, but on the whole sub-pixel matching in a Fourier space (while Nyquist assumption is not true) seems roughly on par with matching in the spatial domain - I would love to see something that explains why this is so
Happy to discuss this more as I am still learning
My specific question is "If I am matching a RAW stereo block (say 8x8 or 16x16 block), for sub-pixel resolution matching from two similar cameras, why (if at all) is it better to use DCT/Fourier matching e.g. matlab normxcorr2 or the elphel guys https://blog.elphel.com/2018/01/complex-lapped-transform-bay... , rather than try to match in the spatial domain (linear algebra). On writing this I suppose I better look at the H.264 and HEVC algos and see if they work in the frequency or spatial domain when they are looking at sub-pixel matches.
Astro-photography is an illustrative example. Celestial objects don't appear in night sky scenes with a regular frequency. So there is no need for an anti-aliasing filter. It just lowers point resolution without any benefit. So cameras for astro-photography often lack AA filters.
+ For what it's worth, ringing artifacts are often caused by diffraction or lens aberrations rather than aliasing from sampling frequency (though they can be).
+ The shape of the modulation transfer function (MTF) is different between film and digital. The line pair comparison in the article illustrates it without being explicit. The Nyquist limit means that sharpness falls off a cliff in digital images and the example line pairs go from resolved to gray abruptly. The example line pairs for film fade to gray, what is and isn't sharp is determined by observation rather than an equation. Digital images often appear sharper because they are only ever so sharp and film appears softer because it is kinda sorta sharp beyond the point where it is clearly sharp. It's why film has a different look...or is a different artistic medium than digital when it comes to some forms of expression.