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Wang et al. Microstructures 2023;3:2023036 https://dx.doi.org/10.20517/microstructures.2023.27 Page 7 of 12

We further inspected the local intensity information in the raw data and the Auto-CLAHE-processed data
to evaluate the impact of signal enhancement on forbidden reflections and noise in diffraction patterns.
Figure 4 depicts the selected diffraction patterns before and after Auto-CLAHE processing, together with
the intensity profile plotted along the [0002] direction, indicated by the dashed boxes. Mg has the hexagonal
closed-packed (HCP) crystal structure; hence, the even-numbered reflections (e.g., 0002, 0004, etc.) are
allowed, and the odd-numbered reflections (e.g., 0001, 0003, etc.) are forbidden. However, due to the TEM
foil thickness and the limited 0.3° precession angle, some dynamical effects persist, making the forbidden
reflections visible but with reduced intensity (grey curves in the intensity profiles for Patterns 1 and 3). Even
after the Auto-CLAHE signal enhancement, the lower intensity of the forbidden reflection is retained
(maroon curves in the intensity profiles for Patterns 1 and 3). For Pattern 2, due to the specific crystal
orientation, the reflection has a higher intensity than the 0002 reflection in the raw data. This
information is preserved in the Auto-CLAHE-processed diffraction pattern. The above observations
indicate that although the CLAHE algorithm amplifies the weak signals more than the strong ones, it does
not arbitrarily amplify weak spots to the detriment of crucial structural information, particularly regarding
the relative diffraction spot intensities influenced by structure factors. One should bear in mind that with
the signal enhancement, the background level in the vicinity of the diffraction spots can increase. The
background is largely attributed to the overlapping of Gaussian profile tails of the diffraction spots, and its
level can be assessed by the intensity at the troughs between the peaks. In all cases, the background levels are
higher in the Auto-CLAHE processed diffraction patterns than in raw data. However, the adverse effect of
the increased background level in diffraction patterns seems to be outweighed by the benefits gained from
signal enhancement, which is evidenced by the increase in IQ after Auto-CLAHE processing, as shown in
Figure 3. It is also interesting to observe the preservation of very low noise/background levels from the
regions in the raw data to the auto-CLAHE-enhanced data, indicated by the black arrows in the intensity
profiles in Patterns 2 and 3 [Figure 4]. This provides assurance that the CLAHE algorithm does not
introduce noise arbitrarily into areas of low noise in diffraction patterns.

To test the overall effectiveness of the Auto-CLAHE algorithm, crystal orientation maps were constructed
using both raw and enhanced datasets. Figure 5A shows the raw data-constructed orientation maps along
the X, Y, and Z directions overlayed with the index maps. The orientations map is created by comparing the
experimentally acquired diffraction patterns with the simulated diffraction patterns via template matching.
The index map is created by comparing the experimentally acquired diffraction patterns with the simulated
ones, with better fits corresponding to brighter pixels. It is very similar to the band contrast map in EBSD.
The quality of the orientation maps constructed from the raw data is acceptable. The maps reveal the overall
deformed matrix and deformation twinning in the top right corner. (The very top layer is the Pt protective
coating, and its indexing information should be discarded.) The deformed microstructure is expected as the
plastic deformation of c-axis compressed Mg is predominantly accommodated by <c+a> dislocations [27,28] .
The complex stress state from nanoindentation also activated some deformation twinning [29,30] . However, it
is also apparent that regions of noise are present in both the matrix and the twin, as indicated by the white
arrows. This noise is caused by a lack of clarity in the diffraction spots of the raw data. With the obscured
diffraction spots, the commercial orientation indexing software is unable to accurately match the
experimental data with the simulated ones, resulting in erroneous orientation assignments in several
diffraction patterns. Figure 5B shows the Auto-CLAHE-enhanced orientation maps overlayed with the
index maps. After preprocessing the dataset using Auto-CLAHE, a vast majority of the noise in the
orientation maps is removed, as distinct diffraction spots are revealed in the diffraction patterns, allowing
for accurate orientation indexing. Additionally, it is worth pointing out that there is a region in the raw data
that appears to be a twin grain, as indicated by the black arrows in Figure 5A. However, in the raw data, it is
difficult to distinguish if it is actually present or merely an extension of the noise. In contrast, the filtered