Background noise removal in x-ray ptychography
Bibliographic record
Abstract
Ptychography is a diffraction-based x-ray microscopy method that removes the resolution limit imposed by image-forming optical elements. However, background noise in the recorded diffraction patterns will degrade the reconstructed images and may cause reconstruction failure. Removal of the background noise from a ptychography dataset is an important but rather ambiguous prereconstruction data processing step because high-spatial-frequency diffraction signals are inevitably partly wiped out along with the noise. In this paper, several newly designed techniques for removing background noise from experimental ptychographic datasets are provided. Meanwhile, effects of residual background noise and high-frequency signal loss on reconstructed image quality are discussed in detail. The image quality is assessed quantitatively by the power spectral density analysis method and spatial resolution calculation. Both the simulated and experimental results indicate that the positive effect of noise removal by these methods clearly exceeds the negative effect of the accompanied high-spatial-frequency signal loss because part of the lost signals can be recovered by the improved consistencies between neighboring diffraction patterns by the noise removal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".