Ptychography is a lensless imaging method that allows for wavefront sensing and phase-sensitive microscopy from a set of diffraction patterns. Recently, it has been shown that the optimization task in ptychography can be achieved via automatic differentiation (AD). Here, we propose an open-access AD-based framework implemented with TensorFlow, a popular machine learning library. Using simulations, we show that our AD-based framework performs comparably to a state-of-the-art implementation of the momentum-accelerated ptychographic iterative engine (mPIE) in terms of reconstruction speed and quality. AD-based approaches provide great flexibility, as we demonstrate by setting the reconstruction distance as a trainable parameter. Lastly, we experimentally demonstrate that our framework faithfully reconstructs a biological specimen.
@article{arxiv.2010.02074,
title = {Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation},
author = {Jacob Seifert and Dorian Bouchet and Lars Loetgering and Allard P. Mosk},
journal= {arXiv preprint arXiv:2010.02074},
year = {2021}
}