  With the advent of interferometric instruments with 4 telescopes at the VLTI
and 6 telescopes at CHARA, the scientific possibility arose to routinely obtain
milli-arcsecond scale images of the observed targets. Such an image
reconstruction process is typically performed in a Bayesian framework where the
function to minimize is made of two terms: the datalikelihood and the Bayesian
prior. This prior should be based on our prior knowledge of the observed
source. Up to now,this prior was chosen from a set of generic and arbitrary
functions, such as total variation for example. Here, we present an image
reconstruction framework using generative adversarial networks where the
Bayesian prior is defined using state-of-the-art radiative transfer models of
the targeted objects. We validate this new image reconstruction algorithm on
synthetic data with added noise. The generated images display a drastic
reduction of artefacts and allow a more straight forward astrophysical
interpretation. The results can be seen as a first illustration of how neural
networks can provide significant improvements to the image reconstruction of a
variety of astrophysical sources.
