Most are, but in biology/physics STED[1] and STORM are physics based methods for overcoming the diffraction limit[2]. STED is pure physics, no math/deconvolution/AI tricks.

[1] https://en.wikipedia.org/wiki/STED_microscopy

[2] https://en.wikipedia.org/wiki/Super-resolution_microscopy

They use extra tricks at the image capture level to supercharge how much information you can load into the captured images (and then decipher them), but the methods are still related at least in STORM - you’re effectively deconvolving lots of sparse images and then merging them! Gaussian fitting of point sources is literally dexonvolution right? You’re just estimating the psf as a 2d Gaussian!

I am not qualified to get too in the weeds on the physics, but 'Resolution' is... complicated. Usually, when we talk about resolution we are talking about the ability to distinguish two points.

The 'resolution limit' (Abbe diffraction limit [1]) is related to a few things, but practically by the wavelength of the excitation light and the numerical aperture (NA) of the lens (d = wavelength/2NA). When we (physicists/biologists) say 'super resolution', we mean resolving things smaller than what was previously possible based on the Abbe diffraction limit. So rather than only being able to resolve two points separated by a minimum of 174nm with a 488nm laser and a 1.4NA objective, we can resolve particles separated by as little as 40-70nm with STED (but it varies in practice).

STED does not accomplish this by estimating PSFs and fitting Gaussians, it uses a doughnut shaped depleting laser to force surrounding fluorescence sources to a 'depleted' state, and an excitation laser to excite a much smaller point in the middle of the depletion (see the doughnut in the STED wikipedia page, Stephen Hell and Thomas Klar won the Nobel Prize in Chemistry for this in 1999 [2].

I know PALM/STORM uses statistics, blinking fluorescence point sources, and long imaging times to build up a super resolution image based on the point sources and computational reconstruction.

Not as familiar with that one or SIM, but I know the "Pure physics/optics" folks I work with regard STED as the most pure physics based one that doesn't rely on fitting, deconvolution, or tricks (not that any of that is bad or wrong!).

[1] https://en.wikipedia.org/wiki/Diffraction-limited_system#The... [2] https://en.wikipedia.org/wiki/STED_microscopy

No these are not deconvolution at all. They are using AI to add information to the image.

Superresolution in consumer cameras is definitely adding detail to the image using prior information about the universe (hair has a pattern, what looks like an edge has to be sharper than what the image says, etc). This is definitely the questionable artsy aspect of this superres boom. But the question I answered was more specific which supposed how you can make up info which doesn’t exist, but that’s definitely not true either. Modern superres tech (especially the non-deep learning kind) can extract more info from the image if you systematically account for the psf of the camera, distortions etc.

What makes you so confident that this "AI" doesn't end up just doing deconvolution?

Because the images being enhanced are in focus