  Phase retrieval, or the process of recovering phase information in reciprocal
space to reconstruct images from measured intensity alone, is the underlying
basis to a variety of imaging applications including coherent diffraction
imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and
hence, are time-consuming and computationally expensive, precluding real-time
imaging. Furthermore, iterative phase retrieval algorithms struggle to converge
to the correct solution especially in the presence of strong phase structures.
In this work, we demonstrate the training and testing of CDI NN, a pair of deep
deconvolutional networks trained to predict structure and phase in real space
of a 2D object from its corresponding far-field diffraction intensities alone.
Once trained, CDI NN can invert a diffraction pattern to an image within a few
milliseconds of compute time on a standard desktop machine, opening the door to
real-time imaging.
