Reducing misfocus-related motion artefacts in laser speckle contrast imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Laser Speckle Contrast Imaging (LSCI) is a flexible, easy-to-implement technique for measuring blood flow speeds in-vivo. In order to obtain reliable quantitative data from LSCI the object must remain in the focal plane of the imaging system for the duration of the measurement session. However, since LSCI suffers from inherent frame-to-frame noise, it often requires a moving average filter to produce quantitative results. This frame-to-frame noise also makes the implementation of rapid autofocus system challenging. In this work, we demonstrate an autofocus method and system based on a novel measure of misfocus which serves as an accurate and noise-robust feedback mechanism. This measure of misfocus is shown to enable the localization of best focus with sub-depth-of-field sensitivity, yielding more accurate estimates of blood flow speeds and blood vessel diameters.
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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 | 0.000 | 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