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

Longlong Wu, Seong‐Min Bak, Youngho Shin, Yong S. Chu, Shinjae Yoo, Ian Robinson, Xiaojing Huang

npj Computational Materials 2023 article cited by 22

Source Open-access PDF More in Engineering & Materials

importance 4/10 solved Advanced X-ray Imaging Techniques X-ray microscopy stage: research

Problem

X-ray fluorescence microscopy resolution is limited by the X-ray probe size, unlike diffraction-limited ptychography.

Why it matters

Probe-limited resolution restricts chemical imaging of functional materials used in technology and energy research.

What the AI did

A machine learning model decouples the X-ray probe's impact from the XRF signal, enhancing spatial resolution in simulated and experimental data, including tomography reconstructions.

Results

Enhanced resolution on simulated and experimental XRF data, superior to state-of-the-art scanning XRF; numeric values not stated in source

Impact

Peer-reviewed demonstration claiming broad applicability to XRF microscopy and tomography.

Record

Venue	npj Computational Materials
Published	2023
Work type	article
Authors	Longlong Wu, Seong‐Min Bak, Youngho Shin, Yong S. Chu, Shinjae Yoo, Ian Robinson, Xiaojing Huang
Citations	22 (OpenAlex, at import time)
References	61
Model or method	deep residual networks
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41524-023-00995-9
Open access	gold
Keywords	X-ray fluorescence, deep residual network, super-resolution, ptychography, tomography

Importance rating

4 / 10

real but narrower advance

Resolution improvement for XRF microscopy of functional and biological materials; a real but specialized imaging advance, no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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