It looks like gjm already explained how you’re giving a misleading account of what these algorithms do and how Dr. Bauman used them in a comment 18 days ago:
The “weirdness” term in the CHIRP algorithm is a so-called “patch prior”, which means that you get it by computing individual weirdness measures for little patches of the image, and you do that over lots of patches that cover the image, and add up the results. (This is what she’s trying to get at with the business about random image fragments.) The patches used by CHIRP are only 8x8 pixels, which means they can’t encode very much in the way of prejudices about the structure of a black hole.
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For CHIRP, they have a way of building a patch prior from a large database of images, which amounts to learning what tiny bits of those images tend to look like, so that the algorithm will tend to produce output whose tiny pieces look like tiny pieces of those images. You might worry that this would also tend to produce output that looks like those images on a larger scale, somehow. That’s a reasonable concern! Which is why they explicitly checked for that. (That’s what is shown by the slide from the TEDx talk that I thought might be misleading you, above.) The idea is: take several very different large databases of images, use each of them to build a different patch prior, and then run the algorithm using a variety of inputs and see how different the outputs are with differently-learned patch priors. And the answer is that the outputs look almost identical whatever set of images they use to build the prior. So whatever features of those 8x8 patches the algorithm is learning, they seem to be generic enough that they can be learned equally well from synthetic black hole images, from real astronomical images, or from photos of objects here on earth.
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Oh, a bonus: you remember I said that one extreme is where the “weirdness” term is zero, so it definitely doesn’t import any problematic assumptions about the nature of the data? Well, if you look at the CalTech talk at around 38:00 you’ll see that Bouman actually shows you what you get when you do almost exactly that. (It’s not quite a weirdness term of zero; they impose two constraints, first that the amount of emission in each place is non-negative, and second a “field-of-view constraint” which I assume means that they’re only interested in radio waves coming from the region of space they were actually trying to measure. … And it still looks pretty decent and produces output with much the same form as the published image.
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Bouman says (CalTech, 16:00) “the CLEAN algorithm is guided a lot by the user.” Yes, and she is pointing out that this is an unfortunate feature of the (“self-calibrating”) CLEAN algorithm, and a way in which her algorithm is better. (Also, if you listen at about 35:00, you’ll find that they actually developed a way to make CLEAN not need human guidance.)
It looks like gjm already explained how you’re giving a misleading account of what these algorithms do and how Dr. Bauman used them in a comment 18 days ago: