  Ptychography has rapidly grown in the fields of X-ray and electron imaging
for its unprecedented ability to achieve nano or atomic scale resolution while
simultaneously retrieving chemical or magnetic information from a sample. A
ptychographic reconstruction is achieved by means of solving a complex inverse
problem that imposes constraints both on the acquisition and on the analysis of
the data, which typically precludes real-time imaging due to computational cost
involved in solving this inverse problem. In this work we propose PtychoNN, a
novel approach to solve the ptychography reconstruction problem based on deep
convolutional neural networks. We demonstrate how the proposed method can be
used to predict real-space structure and phase at each scan point solely from
the corresponding far-field diffraction data. The presented results demonstrate
how PtychoNN can effectively be used on experimental data, being able to
generate high quality reconstructions of a sample up to hundreds of times
faster than state-of-the-art ptychography reconstruction solutions once
trained. By surpassing the typical constraints of iterative model-based
methods, we can significantly relax the data acquisition sampling conditions
and produce equally satisfactory reconstructions. Besides drastically
accelerating acquisition and analysis, this capability can enable new imaging
scenarios that were not possible before, in cases of dose sensitive, dynamic
and extremely voluminous samples.
