  This paper proposes a novel framework to regularize the highly ill-posed and
non-linear Fourier ptychography problem using generative models. We demonstrate
experimentally that our proposed algorithm, Deep Ptych, outperforms the
existing Fourier ptychography techniques, in terms of quality of reconstruction
and robustness against noise, using far fewer samples. We further modify the
proposed approach to allow the generative model to explore solutions outside
the range, leading to improved performance.
