Oversample or undersample?

[andrew s]

That's interesting, thanks. I didn't know that.

I always thought (perhaps simplistically) that drizzle effectively provides a higher resolution because the grid of sample points, when combined across drizzled images, approximates a sensor with smaller pixels (or equivalently a finer grid). Also, I would imagine that the random-ish shifts between drizzled images will go some way to approximating stochastic sampling on this finer grid, thereby reducing the spikiness of any aliasing artefacts.

[edit] So, thinking this through, if one drizzled a series of images that had been properly down sampled, logically it would not be possible to increase resolution using drizzle...

Martin

Martin, have a look at this and references therein https://en.wikipedia.org/wiki/Super-resolution_imaging#cite_note-users.soe.ucsc.edu-13under the "Aliasing" subsection.

Your description is similar to that given here http://www.stark-labs.com/craig/resources/Articles-&-Reviews/Drizzle_API.pdfbut the fact remains your need under sampled images that contain phase related information i.e. the aliasing artifacts.

There are many ways to mathematically describe the same thing  e.g.  Newtonian , Lagrangian and Hamiltonian classical mechanics.

Not sure what "properly down sampled" means but a bandwidth limited image that was sampled critically or over sampled would not gain from drizzle although it could introduce other artifacts. 

Regards Andrew