# Fourier Ptychographic Neural Network Combined with Zernike Aberration Recovery and Wirtinger Flow Optimization

## TL;DR

This paper introduces a new imaging method that improves high-resolution image quality by correcting optical aberrations using a neural network and advanced optimization techniques.

## Contribution

The novel contribution is a neural network model that integrates Zernike aberration recovery and optimized Wirtinger flow for improved Fourier ptychographic imaging.

## Key findings

- The proposed model effectively improves aberration correction accuracy in Fourier ptychographic microscopy.
- The method maintains good correction performance even in complex imaging scenarios.
- Optical aberration's impact on image quality is significantly reduced using the new approach.

## Abstract

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.