Lensless inline digital holography versus Fourier ptychography: phase estimation of a large transparent bead
Bibliographic record
Abstract
Lensless inline digital holographic microscopy (LI-DHM) and Fourier ptychographic microscopy (FPM) are two widespread quantitative phase imaging (QPI) techniques. They have been employed in various fields, especially for biological slice imaging because of their simplicity in use, stability in structure, and also large field of view. Spherical phase response (for example from HeLa cells) is commonly observed in biological imagery. As a consequence, for calibration and validation purposes, small (several to tenth of microns in diameter) transparent microbeads have been used as standards. Phase imaging of their large counterparts (hundreds of microns in diameter) using either LI-DHM or FPM has not been reported so far. We are aiming to analyze the phase response of a 146-μm soda-lime microsphere. It has been immersed in Canada balsam to reduce phase difference and to avoid overexposed diffraction rings. The phase estimation issue has been tackled using approaches that involve either Gerchberg–Saxton type algorithms or an inverse problem-based procedure. Confronting the results confirms the QPI capability for both imaging techniques to assess phase responses from such a large transparent object.
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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 | 0.000 |
| 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".