PtychoNet: Fast and High Quality Phase Retrieval for Ptychography

Citation

Guan, Z.; Tsai, E.H.R.; Huang, X.; Yager, K.G.; Qin, H. "PtychoNet: Fast and High Quality Phase Retrieval for Ptychography" British Machine Vision Conference 2019, 2019 278.
doi:

Summary

Deep learning methods are used to generate an approximant to conventional ptychography reconstruct methods. The machine-learning approach displays improved robustness on sparse datasets.

Abstract

Ptychography is a coherent diffractive imaging method that captures multiple diffraction patterns of a sample with a set of shifted localized illuminations (“probes”). The reconstruction problem, known as “phase retrieval”, is typically solved by iterative algorithms. In this paper, we propose PtychoNet, a deep learning based method to perform phase retrieval for ptychography in a non-iterative manner. We devise a generative network to encode a full ptychography scan, reverse the diffractions at each scanning point and compute the amplitude and phase of the object. We demonstrate successful reconstructions using PtychoNet as well as recovering fine features in the case of extreme sparse scanning where conventional iterative methods fail to give recognizable features.