Data preprocessing methods for robust Fourier ptychographic microscopy

Yan Zhang; An Pan; Ming Lei; Baoli Yao

doi:10.1117/1.OE.56.12.123107

15 December 2017 Data preprocessing methods for robust Fourier ptychographic microscopy

Yan Zhang, An Pan, Ming Lei, Baoli Yao

Author Affiliations +

Funded by: Natural Science Foundation of China, National Natural Science Foundation of China (NSFC), Natural Science Foundation of China (NSFC)

Optical Engineering, Vol. 56, Issue 12, 123107 (December 2017). https://doi.org/10.1117/1.OE.56.12.123107

Abstract

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique that achieves gigapixel images with both high resolution and large field-of-view. In the current FPM experimental setup, the dark-field images with high-angle illuminations are easily overwhelmed by stray lights and background noises due to the low signal-to-noise ratio, thus significantly degrading the achievable resolution of the FPM approach. We provide an overall and systematic data preprocessing scheme to enhance the FPM’s performance, which involves sampling analysis, underexposed/overexposed treatments, background noises suppression, and stray lights elimination. It is demonstrated experimentally with both US Air Force (USAF) 1951 resolution target and biological samples that the benefit of the noise removal by these methods far outweighs the defect of the accompanying signal loss, as part of the lost signals can be compensated by the improved consistencies among the captured raw images. In addition, the reported nonparametric scheme could be further cooperated with the existing state-of-the-art algorithms with a great flexibility, facilitating a stronger noise-robust capability of the FPM approach in various applications.

Citation Download Citation

Yan Zhang, An Pan, Ming Lei, and Baoli Yao "Data preprocessing methods for robust Fourier ptychographic microscopy," Optical Engineering 56(12), 123107 (15 December 2017). https://doi.org/10.1117/1.OE.56.12.123107

Received: 10 September 2017; Accepted: 28 November 2017; Published: 15 December 2017

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