AutoPhaseNN: unsupervised physics-aware deep learning of 3D | ResearchBunny

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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

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