Real-Time Super-Resolution Imaging System Based on Zero-Shot Learning for Infrared Nondestructive Testing
Bibliographic record
Abstract
Infrared thermography (IRT) and photothermal coherence tomography (PCT) exhibit potential in non-destructive testing and biomedical fields. However, the inevitable heat diffusion significantly affects the sensitivity and resolution of IRT and PCT. Conventional image processing techniques rely on capturing complete thermal sequences, which limits their ability to achieve real-time processes. Here, we construct a real-time super-resolution imaging system based on zero-shot learning strategy for the non-invasive infrared thermography and photothermal coherence tomography techniques. To validate the feasibility and accuracy of this super-resolution imaging system, IRT systems were employed to test several industrial samples and one biomedical sample. The results demonstrated high contrast in the region of interest (ROI) and uncovered valuable information otherwise obscured by thermal diffusion. Furthermore, three-dimensional photothermal coherence tomography was used to validate the excellent denoising and deconvolution capabilities of the proposed real-time super-resolution imaging system.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".