Resolution enhancement for an OCT system applied to multiple-layer information extraction
Bibliographic record
Abstract
Optical coherent tomography (OCT) is a newly developed optical imaging technology that permits high-resolution cross-sectional imaging of an object. Most of the OCT imaging systems is developed for the biomedical applications, such as diagnostics of ophthalmology, dermatology, dentistry and cardiology. The technique behind these applications is the point scanning of laser beam penetrating into an object to obtain the internal features of the object. In this paper, we study a full-field OCT imaging system for acquiring information from a multi-layer information chip. This new system can be used in document security, identification and industrial inspection. Differing from the biology related samples, the information chip consists of a number of thin layers with information coded on their surfaces. The surfaces of the layers are flat and specular with moderate reflectance. The information on one layer is retrieved through demodulating interference image of that layer. To obtain the tomography image of all the layers, the images in each layer are acquired and separated. The axial resolution of the system, usually defined by coherent length of light source, determines how close the separation of the two vicinal layers can be resolved. In this paper we explore a new approach to enhance the axial resolution of the OCT system. The method is based on a three-step phase shift algorithm to solve the tomography images from fused interference patterns. Theoretic study and simulation indicate that the method improves the system resolution and quality of retrieved tomography images for the multilayer information chip. Experiment results are also given in support of the proposed method.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".