Physics

Resolution-enhanced X-ray fluorescence microscopy via deep residual networks

L. Wu, S. Bak, et al.

This groundbreaking research conducted by Longlong Wu, Seongmin Bak, Youngho Shin, Yong S. Chu, Shinjae Yoo, Ian K. Robinson, and Xiaojing Huang introduces a novel machine learning approach to significantly enhance the spatial resolution of X-ray fluorescence microscopy, transforming low-resolution images into high-quality super-resolved outputs. Discover how this innovative technique improves XRF tomography reconstructions for vital materials like LiNi0.6Mn0.2Co0.2O2 particles.

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Abstract

This paper presents a machine learning (ML)-based method to enhance the spatial resolution of X-ray fluorescence (XRF) microscopy by decoupling the X-ray probe's impact from the XRF signal. A residual dense network (RDN) model is trained to transform low-resolution (LR) XRF images into super-resolved images, improving resolution for both simulated and experimental data. The method shows superior performance compared to conventional scanning XRF and is demonstrated on LiNi0.6Mn0.2Co0.2O2 (NMC) particles, significantly improving XRF tomography reconstructions.

Publisher

npj Computational Materials

Published On

Mar 25, 2023

Authors

Longlong Wu, Seongmin Bak, Youngho Shin, Yong S. Chu, Shinjae Yoo, Ian K. Robinson, Xiaojing Huang

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