Stopped reading after the first caption, because it says:
A new AI model (right) adds further details to the first-ever images of black holes
The model doesn't add "further" details. It composites a pretty little picture and puts it into the image. It's not what the black hole actually looks like. The "details" are not "further details", they're fabricated. Nonsense.
To clarify: they used data which was determined as unusable as it was too noisy for regular algorithms. So the neural net was trained on more data than just a couple of images. They were trained on all the raw data available.
Even if the results might not be that accurate, it is actually a good way of solving this type of problem.
Scientifically speaking, the results are not accurate, but it might give us a new perspective to the problem.
Kalman filters can be used to filter noise from multivariate data, and it's just a simple matrix transform, no risk of hallucinations.
Neutral networks are notoriously bad at dealing with small data sets. They "overfit" the data, and create invalid extrapolations when new data falls just a little outside training parameters. The way to make them useful is to have huge amounts of data, train the model on a small portion of it and use the rest to validate.