  Deep neural networks (DNNs) are efficient solvers for ill-posed problems and
have been shown to outperform classical optimization techniques in several
computational imaging problems. DNNs are trained by solving an optimization
problem implies the choice of an appropriate loss function, i.e., the function
to optimize. In a recent paper [A. Goy \textit{et al.}, Phys. Rev. Lett.
121(24), 243902 (2018)], we showed that DNNs trained with the negative Pearson
correlation coefficient as the loss function are particularly fit for
photon-starved phase retrieval problems, which are fundamentally affected by
strong Poison noise. In this paper we demonstrate that the use of a perceptual
loss function significantly improves the reconstructions.
