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

Improved ACOM pattern matching in 4D STEM through adaptive sub pixel peak detection and image reconstruction