Data preprocessing methods for robust Fourier ptychographic microscopy

TL;DR

This paper introduces a comprehensive data preprocessing scheme for Fourier ptychographic microscopy that effectively reduces noise and stray light, significantly improving image reconstruction quality and robustness in experimental setups.

Contribution

The paper presents a systematic, non-parametric preprocessing method for noise removal in FP datasets, enhancing reconstruction quality and compatibility with existing algorithms.

Findings

Enhanced image quality through noise and stray light removal

Improved robustness of FP reconstruction in noisy conditions

Demonstrated effectiveness in experimental setups

Abstract

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging technique with both high resolution and wide field-of-view. In current FP experimental setup, the dark-field images with high-angle illuminations are easily submerged by stray light and background noise due to the low signal-to-noise ratio, thus significantly degrading the reconstruction quality and also imposing a major restriction on the synthetic numerical aperture (NA) of the FP approach. To this end, an overall and systematic data preprocessing scheme for noise removal from FP's raw dataset is provided, which involves sampling analysis as well as underexposed/overexposed treatments, then followed by the elimination of unknown stray light and suppression of inevitable background noise, especially Gaussian noise and CCD dark current in our experiments. The reported non-parametric scheme facilitates great enhancements of the FP's performance, which has been demonstrated experimentally that the benefits of noise removal by these methods far outweigh its defects of concomitant signal loss. In addition, it could be flexibly cooperated with the existing state-of-the-art algorithms, producing a stronger robustness of the FP approach in various applications.