Computational approach for correcting defocus and suppressing speckle noise in line-field optical coherence tomography images
Bibliographic record
Abstract
The trade-off between transverse resolution and depth-of-focus (DOF) typical for optical coherence tomography (OCT) systems based on conventional optics, prevents “single-shot” acquisition of volumetric OCT images with sustained high transverse resolution over the entire imaging depth. Computational approaches for correcting defocus and higher order aberrations in OCT images developed in the past require highly stable phase data, which poses a significant technological challenge. Here, we present an alternative computational approach to sharpening OCT images and reducing speckle noise, based on intensity OCT data. The novel algorithm uses non-local priors to model correlated speckle noise within a maximum a posteriori framework to generate sharp and noise-free images. The performance of the algorithm was tested on images of plant tissue (cucumber) and in-vivo healthy human cornea, acquired with line-field spectral domain OCT (LF-SD-OCT) systems. The novel algorithm effectively suppressed speckle noise and sharpened or recovered morphological features in the OCT images for depths up to 13×DOF (depth-of-focus) relative to the focal plane.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".