  Phase retrieval approaches based on DL provide a framework to obtain phase
information from an intensity hologram or diffraction pattern in a robust
manner and in real time. However, current DL architectures applied to the phase
problem rely i) on paired datasets, i.e., they are only applicable when a
satisfactory solution of the phase problem has been found, and ii) on the fact
that most of them ignore the physics of the imaging process. Here, we present
PhaseGAN, a new DL approach based on Generative Adversarial Networks, which
allows the use of unpaired datasets and includes the physics of image
formation. Performance of our approach is enhanced by including the image
formation physics and provides phase reconstructions when conventional phase
retrieval algorithms fail, such as ultra-fast experiments. Thus, PhaseGAN
offers the opportunity to address the phase problem when no phase
reconstructions are available, but good simulations of the object or data from
other experiments are available, enabling us to obtain results not possible
before.
