NLPAR: Non-local smoothing for enhanced EBSD pattern indexing

•A newpost-processing technique for EBSD patternsis proposed.•The non-local smoothing uses larges kernels but preserves edges and sharp features.•By improving signal-to-noise ratios, indexing success rates are greatly improved.•Capabilities demonstrated through application to nickel and aluminum mat...

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Bibliographic Details
Published in:Ultramicroscopy 2019-05, Vol.200, p.50-61
Main Authors: Brewick, Patrick T., Wright, Stuart I., Rowenhorst, David J.
Format: Article
Language:English
Subjects:
EBSD
Kikuchi patterns
Non-local means filtering
Pattern indexing
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Summary:•A newpost-processing technique for EBSD patternsis proposed.•The non-local smoothing uses larges kernels but preserves edges and sharp features.•By improving signal-to-noise ratios, indexing success rates are greatly improved.•Capabilities demonstrated through application to nickel and aluminum materials. Due to continued advances in phosphor sensitivity and camera technology, electron backscattered diffraction (EBSD) within a scanning electron microscope (SEM) has become an increasingly popular method for determining crystal orientations within a given microstructure. Concurrent advances in computational processing have also made it possible to store each individual diffraction pattern as it is collected, which has allowed more complex algorithms to be deployed for post-processing and indexing patterns. This paper proposes a new post-processing technique for pattern enhancement that aids in re-indexing by leveraging a non-local smoothing kernel whose weights are based on the exponential decay of the Euclidean distance between patterns. The advantage of this approach is its ability to utilize very large smoothing kernels without losing integrity near interface boundaries and while still operating on timescales comparable to traditional indexing approaches. Using an Inconel 600 nickel alloy sample, the capabilities and performance of the proposed approach are compared to other indexing schemes, including neighbor pattern averaging with re-indexing (NPAR) and a dictionary-based approach. The results demonstrate that the proposed method consistently produces a higher index success rate (ISR) than NPAR and comparable ISRs to the dictionary-based approach, a method with orders-of-magnitude greater computational demands. Source code for the NLPAR algorithm is available at https://github.com/USNavalResearchLaboratory/NLPAR
ISSN:0304-3991
1879-2723
DOI:10.1016/j.ultramic.2019.02.013