Double‐flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction. Issue 6 (24th February 2021)

• Record Type: Journal Article
• Authors: Sun, Minglu; Shao, Lina; Zhu, Youqiang; Zhang, Yuxi; Wang, Shaoxin; Wang, Yukun; Diao, Zhihui; Li, Dayu; Mu, Quanquan; Xuan, Li
• Is Part Of: Journal of biophotonics. Volume 14:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-24
• Subjects: convolutional neural networks -- deep learning -- Fourier ptychographic -- optical microscopic imaging systems -- rapid large‐FOV reconstructions; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202000444
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Fourier ptychographic microscopy is a promising imaging technique which can circumvent the space‐bandwidth product of the system and achieve a reconstruction result with wide field‐of‐view (FOV), high‐resolution and quantitative phase information. However, traditional iterative‐based methods typically require multiple times to get convergence, and due to the wave vector deviation in different areas, the millimeter‐level full‐FOV cannot be well reconstructed once and typically required to be separated into several portions with sufficient overlaps and reconstructed separately, which makes traditional methods suffer from long reconstruction time for a large‐FOV (of the order of minutes) and limits the application in real‐time large‐FOV monitoring of live sample in vitro. Here we propose a novel deep‐learning based method called DFNN which can be used in place of traditional iterative‐based methods to increase the quality of single large‐FOV reconstruction and reducing the processing time from 167.5 to 0.1125 second. In addition, we demonstrate that by training based on the simulation dataset with high‐entropy property (Opt. Express 28, 24 152 [2020]), DFNN could has fine generalizability and little dependence on the morphological features of samples. The superior robustness of DFNN against noise is also demonstrated in both simulation and experiment. Furthermore, our model shows more robustness against the wave vector deviation. Therefore, we could achieve better results at the edge areas of a single large‐FOV reconstruction. Our method demonstrates a promising way to perform real‐time single large‐FOV reconstructions and provides further possibilities for real‐time large‐FOV monitoring of live samples with sub‐cellular resolution. Abstract : We propose a novel deep‐learning based method called DFNN to perform Fourier Ptychographic microscopy (FPM) reconstrction, DFNN shows fine generalizability for different samples and has stronger robustness against noise and less sensitive to the wave vector deviation which is essential to traditional iterative‐based methods, therefore, DFNN could obtain better reconstructed results at the edge areas of a single large field of view (FOV) reconstruction. Due to the end‐to‐end structure and the graphic processing units acceleration technology, the reconstruction speed could be greatly improved.