$Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks$

Physics

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

L. Wu, S. Yoo, et al.

Discover a groundbreaking 3D machine learning model that enhances phase retrieval accuracy in coherent X-ray diffraction imaging, developed by leading experts Longlong Wu, Shinjae Yoo, Ana F. Suzana, Tadesse A. Assefa, Jiecheng Diao, Ross J. Harder, Wonsuk Cha, and Ian K. Robinson. This innovative approach surpasses traditional methods, offering rapid and precise results for real-time experiments.

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Abstract

This paper proposes a 3D machine learning (ML) model, combining supervised and transfer learning, for phase retrieval in coherent X-ray diffraction imaging (CDI). The model significantly improves the accuracy of 3D morphological information recovery from experimental Bragg CDI data compared to traditional iterative methods. The model can provide rapid, accurate results beneficial for real-time experiments and can be used without prior training, leveraging a self-defined loss function for robust phase retrieval.

Publisher

npj Computational Materials

Published On

Oct 28, 2021

Authors

Longlong Wu, Shinjae Yoo, Ana F. Suzana, Tadesse A. Assefa, Jiecheng Diao, Ross J. Harder, Wonsuk Cha, Ian K. Robinson

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