Periodic Artifacts Generation and Suppression in X-ray Ptychography
Bibliographic record
Abstract
As a unique coherent diffraction imaging method, X-ray ptychography has an ultrahigh resolution of several nanometers for extended samples. However, ptychography is often degraded by various noises that are mixed with diffracted signals on the detector. Some of the noises can transform into periodic artifacts (PAs) in reconstructed images, which is a basic problem in raster-scan ptychography. Herein, we propose a novel periodic-artifact suppressing algorithm (PASA) and present a new understanding of PAs or raster-grid pathology generation mechanisms, which include static intensity (SI) as an important cause of PAs. The PASA employs a gradient descent scheme to iteratively separate the SI pattern from original datasets and a probe support constraint applied in the object update. Both simulative and experimental data reconstructions demonstrated the effectiveness of the new algorithm in suppressing PAs and improving ptychography resolution and indicated a better performance of the PASA method in PA removal compared to other mainstream algorithms. In the meantime, we provided a complete description of SI conception and its key role in PA generation. The present work enhances the feasibility of raster-scan ptychography and could inspire new thoughts for dealing with various noises in ptychography.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".