Multi-Angle Plasma Focused Ion Beam (FIB) Curtaining Artifact Correction Using a Fourier-Based Linear Optimization Model
Why this work is in the frame
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Bibliographic record
Abstract
We present a flexible linear optimization model for correcting multi-angle curtaining effects in plasma focused ion beam scanning electron microscopy (PFIB-SEM) images produced by rocking-polishing schemes. When PFIB-SEM is employed in a serial sectioning tomography workow, it is capable of imaging large three-dimensional volumes quickly, providing rich information in the critical 10-100 nm feature length scale. During tomogram acquisition, a "rocking polish" is often used to reduce straight-line "curtaining" gradations in the milled sample surface. While this mitigation scheme is effective for deep curtains, it leaves shallower line artifacts at two discretized angles. Segmentation and other automated processing of the image set requires that these artifacts be corrected for accurate microstructural quantification. Our work details a new Fourier-based linear optimization model for correcting curtaining artifacts by targeting curtains at two discrete angles. We demonstrate its capabilities by processing images from a tomogram from a multiphase, heterogeneous concrete sample. We present methods for selecting the parameters which meet the user's goals most appropriately. Compared to previous works, we show that our model provides effective multi-angle curtain correction without introducing artifacts into the image, modifying non-curtain structures or causing changes to the contrast of voids. Our algorithm can be easily parallelized to take advantage of multi-core hardware.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it