I'm working on a focus stacking program, I wrote one a few years ago which used a very simple method, basically taking a DoG approach, but only operating on the single full resolution image. The resulting images had a soft focus look, kind of dreamy.
This time I researched further and found papers describing image pyramids, essentially doing the same thing as before except on multiple spatial frequencies, then recombining them with the maximum components which I imagine will solve the soft focus look.
But learning about pyramid based approaches led me to scale-space representation and multi-resolution analysis, and I can't tell if I should pursue one of those methods for possibly better results? (And MRA led me to wavelet transforms which I also don't see how I would implement.)
Furthermore, I don't really understand how either would even be implemented for focus stacking, but my impression of scale-space is that in practice I would just end up building a pyramid anyway, but that doesn't seem right. So am I missing something about that?
And does it seem like there is anything to be gained by using a newer or more sophisticated approach than image pyramids for focus stacking?
(I used Python last time but am using Apple's CoreImage framework this time, so I might be a bit more limited now, maybe MRA isn't even an option.)
Thanks in advance!