Predicting ptychography probe positions using single-shot phase retrieval neural network

TL;DR

This paper introduces a neural network-based method for predicting probe positions in ptychography with large errors, improving reconstruction accuracy when traditional optimization methods fail due to error accumulation.

Contribution

A novel neural network approach for single-shot phase retrieval that accurately predicts probe positions in ptychography with large errors, bypassing convergence issues of existing methods.

Findings

Achieves accurate position prediction with errors around 100 pixels.

Effective for data with large, accumulating position errors.

Applicable to ptychography setups lacking advanced position control.

Abstract

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of $10^2$ pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.