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Wang et al. Microstructures 2023;3:2023036 https://dx.doi.org/10.20517/microstructures.2023.27 Page 9 of 12

Figure 6. Selected diffraction patterns and reconstructed x-direction orientation maps using the (A) raw data and Auto-CLAHE
algorithm-processed data with (B) 1, (C) 10, and (D) 100 numerator coefficients.

It is worth noting that determination of the clip limit using the inverse relationship in the equation
is based on empirical considerations. Here, we explore the effect of changing
the numerator coefficient on the qualities of diffraction patterns and orientation maps. Figure 6A depicts
the selected diffraction patterns and the corresponding orientation map from the raw data. Noise is
apparent in both the matrix and the twin, as previously described. In contrast, all Auto-CLAHE-processed
data demonstrate much reduced noise in the respective orientation maps compared to the raw data. When
choosing 1 as the numerator coefficient, some visible enhancements of the diffraction pattern were
observed, and most noise in the orientation map was removed [Figure 6B]. However, low noise level persists
in the matrix and the twin (some indicated by the white dashed circles). Increasing the numerator
coefficient to 10 (the one we adopted in our algorithm) further enhances the diffraction pattern signals and
leads to a near-noise-free orientation map [Figure 6C]. Further increasing the numerator coefficient to 100
results in over-saturation of the diffraction spots and a noisier orientation map [Figure 6D]. The most
notable noise is the misidentification of the twinned region as the matrix in the top right part of the map;
some noise also appears in the matrix (indicated by the white circles). This is caused by the poorer indexing
from the diffraction spot supersaturation, diffraction spots merging, and arbitrarily augmented background.
Taken together, these observations are encouraging, suggesting that the Auto-CLAHE algorithm is relatively
robust - improved orientation maps can be generated with a wide range selection of the numerator
coefficients. However, carefully selecting the numerator coefficient is critical to creating orientation maps
with high confidence and low noise. Moreover, we encourage the users to try different numerator
coefficients or even different inverse relationships (e.g., ) based on the
diffraction quality to achieve optimal indexing results.

Furthermore, we point out that the NanoMEGAS ASTAR commercial software offers the flexibility of
adjusting brightness, contrast, and gamma settings after data acquisition before data reconstruction.
However, an equal degree of enhancement (determined by the user) is uniformly applied to all acquired
diffraction patterns. For instance, a 50% increase in brightness can effectively enhance faint diffraction spots