Processing and indexing of electron backscatter patterns using open-source software

A new method to increase the signal-to-noise ratio S/N of electron backscatter patterns (EBSPs) based upon principal component analysis (PCA) is presented. The PCA denoising method is applied to ten scans of EBSPs from the same region of interest of a recrystallised nickel sample acquired with a decreasing S/N , achieved by reducing the exposure time while increasing the camera gain accordingly. That PCA denoising increases S/N in EBSPs is demonstrated by comparing indexing success rates after both Hough and dictionary indexing (HI and DI) of the Ni patterns having undergone one of four processing routes: i) standard static and dynamic background corrections (standard corrections), ii) standard corrections and pattern averaging with the four closest neighbours, iii) standard corrections and PCA denoising, and iv) standard corrections and pattern averaging followed by PCA denoising. Both pattern averaging and PCA denoising increases the indexing success rates for both indexing approaches for the studied Ni scans, with the former processing route providing the better success rates. The best success rates are obtained after pattern averaging followed by PCA denoising. The potential of PCA denoising to reveal additional pattern details compared to standard corrections and pattern averaging is demonstrated in a pattern from an orthoclase (KAlSi 3 O 8 ) grain in a geological sample. Software code, and the Ni data sets, are released alongside this article as part of KikuchiPy, an open-source software package dedicated to processing and analysis of EBSPs.