  The technique known as 4D-STEM has recently emerged as a powerful tool for
the local characterization of crystalline structures in materials, such as
cathode materials for Li-ion batteries or perovskite materials for
photovoltaics. However, the use of new detectors optimized for electron
diffraction patterns and other advanced techniques requires constant adaptation
of methodologies to address the challenges associated with crystalline
materials. In this study, we present a novel image processing method to improve
pattern matching in the determination of crystalline orientations and phases.
Our approach uses sub-pixelar adaptative image processing to register and
reconstruct electron diffraction signals in large 4D-STEM datasets. By using
adaptive prominence and linear filters such as mean and gaussian blur, we are
able to improve the quality of the diffraction pattern registration. The
resulting data compression rate of 103 is well-suited for the era of big data
and provides a significant enhancement in the performance of the entire ACOM
data processing method. Our approach is evaluated using dedicated metrics,
which demonstrate a high improvement in phase recognition. Our results
demonstrate that this data preparation method not only enhances the quality of
the resulting image but also boosts the confidence level in the analysis of the
outcomes related to determining crystal orientation and phase. Additionally, it
mitigates the impact of user bias that may occur during the application of the
method through the manipulation of parameters.
