A Physics-Informed Diffusion Model for Super-Resolved Reconstruction of Optical Coherence Tomography Data
Bibliographic record
Abstract
OBJECTIVE: This study introduces a physics-informed diffusion model (PIDM) for super-resolution (SR) reconstruction of optical coherence tomography (OCT) data. METHODS: An optimization framework was developed for maximizing the likelihood of observing an OCT image in the dataset, given the super-resolved reconstruction from a physics-informed diffusion model (PIDM) that reverses the degradations in OCT images. The image degradations were modeled as a serialization of three processes accounting for the effects of defocus, speckle noise, and digital sampling in OCT images. An analytical model for light-propagation model and a statistical model for speckle noise were derived based on the physical properties of the OCT setup. These models were then integrated with a diffusion model to reverse the degradations caused by defocus blur and digital sampling, minimizing susceptibility to noise and defocus-induced artifacts. RESULTS: The proposed method was employed for reconstructing images of a standard resolution target, plant tissue, and in vivo human cornea, using the complex OCT data acquired with a line-scan OCT (LS-OCT) system. The results from the PIDM exhibit improved sharpness and contrast compared to the images resulting from a few baseline methods such as standalone super-resolution using DM. CONCLUSION: Complementing DM with the physics of OCT could be a viable solution for obtaining high-fidelity SR reconstruction of OCT images. SIGNIFICANCE: This work harnesses the power of diffusion models for super-resolution in OCT images. Such development could potentially enhance cellular-resolution OCT imaging of ophthalmic tissues, where high-fidelity images are crucial for accurate diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".