Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction

doi:10.1002/jbio.202000444

CIOMP OpenIR

	Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction
	M. Sun; L. Shao; Y. Zhu; Y. Zhang; S. Wang; Y. Wang; Z. Diao; D. Li; Q. Mu and L. Xuan
	2021
发表期刊	Journal of Biophotonics
ISSN	1864063X
卷号	14 期号:6
摘要	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. 2021 Wiley-VCH GmbH
DOI	10.1002/jbio.202000444
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65178
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	M. Sun,L. Shao,Y. Zhu,et al. Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction[J]. Journal of Biophotonics,2021,14(6).
APA	M. Sun.,L. Shao.,Y. Zhu.,Y. Zhang.,S. Wang.,...&Q. Mu and L. Xuan.(2021).Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction.Journal of Biophotonics,14(6).
MLA	M. Sun,et al."Double-flow convolutional neural network for rapid large field of view Fourier ptychographic reconstruction".Journal of Biophotonics 14.6(2021).

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