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

Authors:
Open Access
Publisher website
Google Scholar
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.
Funding Information
  • Jilin Provincial Science and Technology Department (2021JBH02L38, YDZJ202301ZYTS180)
  • Science and Technology Development Plan Projects of Jilin Province (2021JBH02L38, YDZJ202301ZYTS180)

This publication has 25 references indexed in Scilit: