Three-dimensional thermophotonic super-resolution imaging by spatiotemporal diffusion reversal methods
Bibliographic record
Abstract
# Truncated correlation photothermal coherence tomography (TC-PCT) image enhancement techniques This repository contains an implementation of diffusion reversal methods for thermo-photonic images. ## Testing Perform deconvolution and save the resulting image for second derivative analysis. Zero-padding the images may be necessary to eliminate edge artifacts. After applying the second derivative, perform spatial adaptive filtering on the selected images. ## Suggested setup The deconvolution and spatial adaptive filtering codes were implemented in \MATLAB version R2021a. The second derivative codes were implemented on Python 3.9.0 installed on Microsoft Windows 10. The following libraries are needed statistics matplotlib cv2 numpy math scipy ## Update notes 9/2023: initial release v1 This project is continuously developing, stay tuned for future developments and new data and/or model releases. For more details, go visit the related manuscript D. Thapa, P. Tavakolian, G. Zhou, A. Zhang, A. Abdelgawad, E. B. Shokouhi, K. Sivagurunathan, A. Mandelis, Three-dimensional thermophotonic super-resolution imaging by spatiotemporal diffusion reversal methods, 2023
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".