Accelerating dictionary indexing of electron backscatter diffraction patterns with PCA and quantization

Dictionary indexing (DI) for Electron Backscatter Diffraction (EBSD) has conventionally traded computational speed for superior robustness to pattern noise when compared to traditional Hough indexing methods. To alleviate this computational bottleneck, we propose a novel Principal Component Analysis based Dictionary Indexing (PCA-DI) approach that uses dimensionality reduction and numerical precision optimization for accelerated pattern matching. A combination of synthetic benchmarks and benchmarks on a standard nickel dataset with varying noise levels demonstrate significant speedup factors over conventional DI that scale with image resolution while maintaining or improving indexing accuracy, particularly under high-noise conditions where dimensionality reduction suppresses noise-dominated high-frequency components. Implementation with reduced numerical precision (FP16 and dynamic INT8 quantization) provides additional performance improvements without compromising indexing quality. The resulting approach enables substantially faster dictionary indexing on consumer-grade hardware, addressing scalability challenges for modern high-resolution EBSD systems and low-symmetry materials.