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ORIGINAL RESEARCH article

Adv. Opt. Technol.
Sec. Optical Imaging
Volume 13 - 2024 | doi: 10.3389/aot.2024.1474654
This article is part of the Research Topic Deep Learning Enhanced Computational Imaging: Leveraging AI for Advanced Image Reconstruction and Analysis View all articles

W1-Net:A highly scalable ptychography convolutional neural network.

Provisionally accepted
Chengye Xing Chengye Xing 1,2Lei Wang Lei Wang 1Yangyang Mu Yangyang Mu 1Yu Li Yu Li 1,3*Guangcai Chang Guangcai Chang 1*
  • 1 Institute of High Energy Physics, Chinese Academy of Sciences (CAS), Beijing, China
  • 2 University of Chinese Academy of Sciences, Beijing, Beijing, China
  • 3 China Spallation Neutron Source, Dongguan, Guangdong, China

The final, formatted version of the article will be published soon.

    X-ray ptychography is a coherent diffraction imaging technique that allows for the quantitative retrieval of both the amplitude and phase information of a sample in diffraction-limited resolution.However, traditional reconstruction algorithms require a large number of iterations to obtain phase and amplitude images exactly, and the expensive computation precludes real-time imaging.To solve the inverse problem of ptychography data, PtychoNN uses deep convolutional neural networks for real-time imaging. However, its model is relatively simple, and its accuracy is limited by the size of the training dataset, resulting in lower robustness. To address this problem, a series of W-Net neural network models have been proposed which can robustly reconstruct the object phase information from the raw data. Numerical experiments demonstrate that our neural network exhibits better robustness, superior reconstruction capabilities and shorter training time with high-precision ptychography imaging.

    Keywords: X-ray ptychography, deep learning, Phase retrieval, real-time imaging, W1-Net

    Received: 02 Aug 2024; Accepted: 11 Oct 2024.

    Copyright: © 2024 Xing, Wang, Mu, Li and Chang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yu Li, Institute of High Energy Physics, Chinese Academy of Sciences (CAS), Beijing, China
    Guangcai Chang, Institute of High Energy Physics, Chinese Academy of Sciences (CAS), Beijing, China

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