  With the next generation of interferometric telescopes, such as the Square
Kilometre Array (SKA), the need for highly computationally efficient
reconstruction techniques is particularly acute. The challenge in designing
learned, data-driven reconstruction techniques for radio interferometry is that
they need to be agnostic to the varying visibility coverages of the telescope,
since these are different for each observation. Because of this, learned
post-processing or learned unrolled iterative reconstruction methods must
typically be retrained for each specific observation, amounting to a large
computational overhead. In this work we develop learned post-processing and
unrolled iterative methods for varying visibility coverages, proposing training
strategies to make these methods agnostic to variations in visibility coverage
with minimal to no fine-tuning. Learned post-processing techniques are heavily
dependent on the prior information encoded in training data and generalise
poorly to other visibility coverages. In contrast, unrolled iterative methods,
which include the telescope measurement operator inside the network, achieve
state-of-the-art reconstruction quality and computation time, generalising well
to other coverages and require little to no fine-tuning. Furthermore, they
generalise well to realistic radio observations and are able to reconstruct the
high dynamic range of these images.
