  Ptychography is a well-established coherent diffraction imaging technique
that enables non-invasive imaging of samples at a nanometer scale. It has been
extensively used in various areas such as the defense industry or materials
science. One major limitation of ptychography is the long data acquisition time
due to mechanical scanning of the sample; therefore, approaches to reduce the
scan points are highly desired. However, reconstructions with less number of
scan points lead to imaging artifacts and significant distortions, hindering a
quantitative evaluation of the results. To address this bottleneck, we propose
a generative model combining deep image priors with deep generative priors. The
self-training approach optimizes the deep generative neural network to create a
solution for a given dataset. We complement our approach with a prior acquired
from a previously trained discriminator network to avoid a possible divergence
from the desired output caused by the noise in the measurements. We also
suggest using the total variation as a complementary before combat artifacts
due to measurement noise. We analyze our approach with numerical experiments
through different probe overlap percentages and varying noise levels. We also
demonstrate improved reconstruction accuracy compared to the state-of-the-art
method and discuss the advantages and disadvantages of our approach.
