  The proposed next-generation Very Large Array (ngVLA) will enable the imaging
of astronomical sources in unprecedented detail by providing an order of
magnitude improvement in sensitivity and angular resolution compared with radio
interferometers currently operating at 1.2--116~GHz. However, the current ngVLA
array design results in a highly non-Gaussian dirty beam that may make it
difficult to achieve high-fidelity images with both maximum sensitivity and
maximum angular resolution using traditional CLEAN deconvolution methods. This
challenge may be overcome with regularized maximum-likelihood (RML) methods, a
new class of imaging techniques developed for the Event Horizon Telescope. RML
methods take a forward-modeling approach, directly solving for the images
without using either the dirty beam or the dirty map. Consequently, this method
has the potential to improve the fidelity and effective angular resolution of
images produced by the ngVLA. As an illustrative case, we present ngVLA imaging
simulations of stellar radio photospheres performed with both multi-scale (MS-)
CLEAN and RML methods implemented in the CASA and SMILI packages, respectively.
We find that both MS-CLEAN and RML methods can provide high-fidelity images
recovering most of the representative structures for different types of stellar
photosphere models. However, RML methods show better performance than MS-CLEAN
for various stellar models in terms of goodness-of-fit to the data, residual
errors of the images, and in recovering representative features in the ground
truth images. Our simulations support the feasibility of transformative stellar
imaging science with the ngVLA, and simultaneously demonstrate that RML methods
are an attractive choice for ngVLA imaging.
