Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

TL;DR

This paper explores using artificial neural networks to enhance the accuracy of propagation-based phase retrieval from simulated intensity measurements, addressing errors from noise, misalignment, and artifacts.

Contribution

The study introduces a neural network approach to improve phase retrieval accuracy in simulated microscopy data, building on existing propagation methods.

Findings

Significant reduction in phase error using neural networks

Effective training with pairs of retrieved and exact phases

Potential for extending to real experimental data

Abstract

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.