  Phase retrieval is a well known ill-posed inverse problem where one tries to
recover images given only the magnitude values of their Fourier transform as
input. In recent years, new algorithms based on deep learning have been
proposed, providing breakthrough results that surpass the results of the
classical methods. In this work we provide a novel deep learning architecture
PR-DAD (Phase Retrieval Using Deep Auto- Decoders), whose components are
carefully designed based on mathematical modeling of the phase retrieval
problem. The architecture provides experimental results that surpass all
current results.
