  Ptychography is an imaging technique that captures multiple overlapping
snapshots of a sample, illuminated coherently by a moving localized probe. The
image recovery from ptychographic data is generally achieved via an iterative
algorithm that solves a nonlinear phase retrieval problem derived from measured
diffraction patterns. However, these iterative approaches have high
computational cost. In this paper, we introduce PtychoDV, a novel deep
model-based network designed for efficient, high-quality ptychographic image
reconstruction. PtychoDV comprises a vision transformer that generates an
initial image from the set of raw measurements, taking into consideration their
mutual correlations. This is followed by a deep unrolling network that refines
the initial image using learnable convolutional priors and the ptychography
measurement model. Experimental results on simulated data demonstrate that
PtychoDV is capable of outperforming existing deep learning methods for this
problem, and significantly reduces computational cost compared to iterative
methodologies, while maintaining competitive performance.
