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AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

Physics

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

Y. Yao, H. Chan, et al.

Discover AutoPhaseNN, a groundbreaking deep learning framework developed by Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, and Mathew J. Cherukara. This innovative approach solves the challenging phase retrieval problem in 3D X-ray Bragg coherent diffraction imaging, achieving a remarkable 100x speedup over traditional methods while preserving high image quality—all without the need for labeled data.

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Playback language: English
Abstract
This paper introduces AutoPhaseNN, a deep learning-based approach for solving the phase retrieval problem in 3D X-ray Bragg coherent diffraction imaging (BCDI). Unlike traditional iterative methods and other deep learning models that require labeled data, AutoPhaseNN incorporates imaging physics into its design and learns to invert 3D BCDI data without labeled data. The model achieves a speedup of approximately 100x compared to iterative methods while maintaining comparable image quality.
Publisher
npj Computational Materials
Published On
Jun 03, 2022
Authors
Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna Balaprakash, Ross J. Harder, Mathew J. Cherukara
Tags
AutoPhaseNN
deep learning
phase retrieval
3D X-ray imaging
Bragg coherent diffraction

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