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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.10555 |
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| _version_ | 1866909957004197888 |
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| author | Nordahl, Gregory Klemmt, Rebekka Bøjesen, Espen Drath |
| author_facet | Nordahl, Gregory Klemmt, Rebekka Bøjesen, Espen Drath |
| contents | We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This method adds an extra time dimension during data collection by recording multiple electron event-sparse diffraction patterns. The resulting datasets are effectively five-dimensional, referred to as locally time-resolved STEM (LTR-STEM). Diffraction data stacks at each scan position are processed using one of two sparsity denoising pipelines: 1) the density-based spatial clustering of applications with noise (DBSCAN) algorithm followed by multi-step persistence thresholding, or 2) sparse principal component analysis (sparse PCA), followed by single-step thresholding. Both methods perform well for diffraction data denoising, as shown by simulated peak signal-to-noise ratio (PSNR) curves, denoised experimental data for virtual imaging, and application-specific denoising for defect detection. PSNR analysis indicates that sparsity-denoised 4D-STEM data reaches the same PSNR as raw data at approximately 16% of the exposure time, demonstrating comparable image quality with a lower dose. In defect detection, a 4.1x increase in sensitivity to relative radial disk shift is observed in the denoised data. Moreover, the LTR-STEM technique may be used to inspect material degradation by tracking changes in diffraction disk intensity, allowing for critical dose estimation and exposure-selective imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Event-sparse stack denoising for 4D-STEM applications Nordahl, Gregory Klemmt, Rebekka Bøjesen, Espen Drath Medical Physics Other Condensed Matter We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This method adds an extra time dimension during data collection by recording multiple electron event-sparse diffraction patterns. The resulting datasets are effectively five-dimensional, referred to as locally time-resolved STEM (LTR-STEM). Diffraction data stacks at each scan position are processed using one of two sparsity denoising pipelines: 1) the density-based spatial clustering of applications with noise (DBSCAN) algorithm followed by multi-step persistence thresholding, or 2) sparse principal component analysis (sparse PCA), followed by single-step thresholding. Both methods perform well for diffraction data denoising, as shown by simulated peak signal-to-noise ratio (PSNR) curves, denoised experimental data for virtual imaging, and application-specific denoising for defect detection. PSNR analysis indicates that sparsity-denoised 4D-STEM data reaches the same PSNR as raw data at approximately 16% of the exposure time, demonstrating comparable image quality with a lower dose. In defect detection, a 4.1x increase in sensitivity to relative radial disk shift is observed in the denoised data. Moreover, the LTR-STEM technique may be used to inspect material degradation by tracking changes in diffraction disk intensity, allowing for critical dose estimation and exposure-selective imaging. |
| title | Event-sparse stack denoising for 4D-STEM applications |
| topic | Medical Physics Other Condensed Matter |
| url | https://arxiv.org/abs/2512.10555 |