NLSTEM: Nonlocal Denoising for Enhanced 4D-STEM Pattern Indexing

4D-STEM-based orientation and phase mapping has enabled rapid microstructure quantification that can be directly combined with standard TEM- and STEM-based imaging modes. Typically, orientation mapping is coupled with beam precession (i.e., precession electron diffraction) to achieve high indexing rates, adding to the cost and often decreasing the spatial resolution of the approach. This paper introduces a new postprocessing approach modeled after the nonlocal pattern averaging and reindexing algorithm developed for the electron backscatter diffraction community, wherein postcollection patterns are averaged using a distance similarity parameter. Results from Ni and Au thin films show that indexing rates can be significantly improved using this postprocessing technique due to improved signal-to-noise ratios in the diffraction patterns. Interestingly, the highest indexing rates are achieved in samples heavily damaged via ion irradiation, suggesting that averaging over curved lattices further improves indexing rates. The source code is made freely available at https://github.com/USNavalResearchLaboratory/PyEBSDIndex.