Fourier ptychography multi-parameter neural network with composite physical priori optimization

TL;DR

This paper introduces a neural network-based approach for Fourier ptychography that optimizes multiple physical parameters simultaneously, reducing system construction and data acquisition constraints for super-resolution microscopy.

Contribution

It proposes a hybrid physical model and data-driven neural network method to recover multiple physical parameters in Fourier ptychography, enhancing practical applicability.

Findings

Successfully recovered system parameters in simulations and experiments.

Reduced requirements for precise system setup and data acquisition.

Improved robustness and efficiency of Fourier ptychography imaging.

Abstract

Fourier ptychography microscopy(FP) is a recently developed computational imaging approach for microscopic super-resolution imaging. By turning on each light-emitting-diode (LED) located on different position on the LED array sequentially and acquiring the corresponding images that contain different spatial frequency components, high spatial resolution and quantitative phase imaging can be achieved in the case of large field-of-view. Nevertheless, FPM has high requirements for the system construction and data acquisition processes, such as precise LEDs position, accurate focusing and appropriate exposure time, which brings many limitations to its practical applications. In this paper, inspired by artificial neural network, we propose a Fourier ptychography multi-parameter neural network (FPMN) with composite physical prior optimization. A hybrid parameter determination strategy combining physical imaging model and data-driven network training is proposed to recover the multi layers of the network corresponding to different physical parameters, including sample complex function, system pupil function, defocus distance, LED array position deviation and illumination intensity fluctuation, etc. Among these parameters, LED array position deviation is recovered based on the features of brightfield to darkfield transition low-resolution images while the others are recovered in the process of training of the neural network. The feasibility and effectiveness of FPMN are verified through simulations and actual experiments. Therefore FPMN can evidently reduce the requirement for practical applications of FPM.