# Accurate phase retrieval of complex point spread functions with deep residual neural networks

## TL;DR

This paper presents a deep residual neural network that accurately and efficiently reconstructs phase information from intensity data for complex point spread functions, enabling rapid optical system analysis.

## Contribution

The study introduces a deep residual neural network approach for phase retrieval of arbitrary PSFs with Zernike-type phase modulations, demonstrating high accuracy and speed.

## Key findings

- Successfully retrieves Zernike coefficients from PSFs at different focal slices.
- Achieves rapid phase reconstruction with high accuracy.
- Applicable to complex optical systems with arbitrary PSFs.

## Abstract

Phase retrieval, i.e. the reconstruction of phase information from intensity information, is a central problem in many optical systems. Here, we demonstrate that a deep residual neural net is able to quickly and accurately perform this task for arbitrary point spread functions (PSFs) formed by Zernike-type phase modulations. Five slices of the 3D PSF at different focal positions within a two micron range around the focus are sufficient to retrieve the first six orders of Zernike coefficients.

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Source: https://tomesphere.com/paper/1906.01748