Multigrid Optimization for Large-Scale Ptychographic Phase Retrieval

TL;DR

This paper introduces a multigrid optimization framework that significantly accelerates large-scale ptychographic phase retrieval, reducing computational costs and outperforming traditional iterative methods.

Contribution

The authors develop a multigrid-based approach that leverages hierarchical structures in ptychography to improve convergence speed and computational efficiency.

Findings

Accelerates convergence of ptychographic phase retrieval algorithms.

Outperforms the traditional Ptychographic Iterative Engine (PIE).

Reduces computational expenses for large-scale imaging.

Abstract

Ptychography is a popular imaging technique that combines diffractive imaging with scanning microscopy. The technique consists of a coherent beam that is scanned across an object in a series of overlapping positions, leading to reliable and improved reconstructions. Ptychographic microscopes allow for large fields to be imaged at high resolution at the cost of additional computational expense. In this work, we propose a multigrid-based optimization framework to reduce the computational burdens of large-scale ptychographic phase retrieval. Our proposed method exploits the inherent hierarchical structures in ptychography through tailored restriction and prolongation operators for the object and data domains. Our numerical results show that our proposed scheme accelerates the convergence of its underlying solver and outperforms the Ptychographic Iterative Engine (PIE), a workhorse in the optics community.