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Recent advances in scanning probe-based tomographic imaging have greatly improved spatial resolution, but systematic and random errors are a serious impediment to reliable data extraction. Here, a combined optimization and alignment algorithm provides a scalable approach to error-correcting reconstruction of large datasets.
Accelerating error correction in tomographic reconstruction