  This paper proposes a new framework to regularize the highly ill-posed and
non-linear phase retrieval problem through deep generative priors using simple
gradient descent algorithm. We experimentally show effectiveness of proposed
algorithm for random Gaussian measurements (practically relevant in imaging
through scattering media) and Fourier friendly measurements (relevant in
optical set ups). We demonstrate that proposed approach achieves impressive
results when compared with traditional hand engineered priors including
sparsity and denoising frameworks for number of measurements and robustness
against noise. Finally, we show the effectiveness of the proposed approach on a
real transmission matrix dataset in an actual application of multiple
scattering media imaging.
