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Deep neural networks (DNNs) are efficient solvers for ill-posed problems andhave been shown to outperform classical optimization techniques in severalcomputational imaging problems. DNNs are trained by solving an optimizationproblem implies the choice of an appropriate loss function, i.e., the functionto 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 Pearsoncorrelation coefficient as the loss function are particularly fit forphoton-starved phase retrieval problems, which are fundamentally affected bystrong Poison noise. In this paper we demonstrate that the use of a perceptualloss function significantly improves the reconstructions.

Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural Networks