I recommend learning about the Fourier Transform, and then peeking at how frequency transformations are used to represent everyday images, like the Cosine Transform used in JPEG.
3 blue 1 brown FFT:
https://youtu.be/spUNpyF58BY
JPEG components
https://en.wikipedia.org/wiki/JPEG#Discrete_cosine_transform
I worked at the Very Large Array radio telescope for a while, and even so the language didn't click for me until I thought about the signal transforms that have become ubiquitous in everyday life.
And a co worker described the telescope as a double-slit experiment, with multiple 2-D holes instead of slits; imagine the interference pattern that you get when you have multiple slits. The elements of an interferometer are the "slits", but they are receiving the light rather than emitting it. The VLA has 27 radio dishes for its elements, so poke 27 holes in a board and shoot a laser through the holes, you get this blurry pattern on the wall. That's the distortion from the shape of your telescope, you're trying to cancel that out...
Then there's the time domain, because the Earth is rotating, so your telescope is sweeping a pattern across the sky. It can seem like a hopeless mess, but it's actually the case that for most observations, that sweep gives you even more coverage of the image. It can be a good thing.
But you have to keep track of one wavefront across all of those moving elements. The correlator is a computer doing those FFTs to find where the signal matches enough between the elements to start extracting finer detail.
I think some insight into how that's possible is around the 8-minute mark of this video:
https://youtu.be/nmgFG7PUHfo?t=8m
I have to admit that I'm still thinking about it all, and it's been 20 years. But JPEG is about at my level.
That's at the 22-minute mark of the same Vertiserum video:
https://youtu.be/nmgFG7PUHfo?t=22m