  One of the most prominent challenges in the field of diffractive imaging is
the phase retrieval (PR) problem: In order to reconstruct an object from its
diffraction pattern, the inverse Fourier transform must be computed. This is
only possible given the full complex-valued diffraction data, i.e. magnitude
and phase. However, in diffractive imaging, generally only magnitudes can be
directly measured while the phase needs to be estimated. In this work we
specifically consider ptychography, a sub-field of diffractive imaging, where
objects are reconstructed from multiple overlapping diffraction images. We
propose an augmentation of existing iterative phase retrieval algorithms with a
neural network designed for refining the result of each iteration. For this
purpose we adapt and extend a recently proposed architecture from the speech
processing field. Evaluation results show the proposed approach delivers
improved convergence rates in terms of both iteration count and algorithm
runtime.
