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in certain pixels. Yet, this same enhancement level might cause oversaturation and compromise indexing
accuracy in pixels containing intense diffraction patterns. In contrast, the auto-CLAHE algorithm employs a
dynamic approach to signal enhancement. It selectively applies signal amplification to pixels that contain
diffraction patterns with low intensity. This ensures that pixel-specific enhancements are tailored to the
actual signal characteristics.

CONCLUSIONS
TEM-based crystal orientation mapping at the nanoscale using PED is a useful tool that can reveal
microstructural information that has been inaccessible to SEM-based EBSD. In this work, we present a new
algorithm named Auto-CLAHE, which automatically amplifies signals of low-intensity diffraction patterns.
The degree of enhancement is tailored dynamically based on the overall intensity of the pattern. The
algorithm applies greater enhancement to patterns with fewer spots located away from zone axes, while little
or no enhancement is used for patterns with many spots located at a zone axis. By enhancing the visibility of
low-intensity diffraction spots, Auto-CLAHE remarkably improves template matching between
experimentally obtained and simulated diffraction patterns, leading to orientation maps with considerably
higher quality and lower noise. Our findings suggest that Auto-CLAHE offers a convenient and efficient
solution for preprocessing PED data. The scientific significance of this work is two-fold. First, our method
improves the diffraction signal, enabling nanoscale orientation mapping in previously challenging systems
with pixels that contain low-intensity diffraction spots. Second, considering that many microscopists are not
trained in digital image processing, our technique serves as an example of successful cross-disciplinary
implementation, demonstrating how knowledge from other research areas can advance electron microscopy
characterization. We anticipate the Auto-CLAHE approach can be routinely applied to all PED datasets and
[31]
potentially be extended to enhance the diffraction signal in 4D-STEM datasets to achieve further
improved analyses.

DECLARATIONS
Acknowledgments
The authors would like to express their gratitude to Dr. Yuwei Zhang and Professor George Pharr for their
invaluable support and guidance on nanoindentation. Additionally, the authors acknowledge the
instrumental and technical support from the Microscopy & Imaging Center (MIC) at Texas A&M
University.

Authors’ contributions
Designing the Auto-CLAHE algorithm and performing data analysis: Wang AL, Hansen MH
Performing sample prep, PED data acquisition, and data analysis: Lai YC, Dong J
Designing the experiment: Xie KY
All authors participated in the writing of the manuscript.

Availability of data and materials
Our source code can be found at https://github.com/lukewang05/Auto-CLAHE. A tutorial on how to use
our code can be found on YouTube: https://www.youtube.com/watch?v=OmUV1fHHfbE.

Financial support and sponsorship
This work was supported by the National Science Foundation (NSF-DMR, grant number 2144973, Program
Manager: Dr. Jonathan Madison).