Real-Time Imaging With Simultaneous Use of Born and Rytov Approximations in Quantitative Microwave Holography
Bibliographic record
Abstract
Microwave and millimeter-wave measurements acquire total-field responses from measurements, yet imaging algorithms instead require the data in the form of scattered-field responses. Two approaches exist for the extraction of the scattered-field data from the total-field responses, namely, the Born and the Rytov data approximations. It is well known that, depending on the target’s size, contrast, and structural complexity, one approximation can achieve an improved accuracy over the other. Yet, the Rytov approximation is rarely used in microwave and millimeter-wave imaging, likely due to phase-unwrapping problems occurring in the case of strongly heterogeneous electrically large targets. Here, we propose a method to utilize the Born and the Rytov approximations simultaneously in a single inversion step for real-time imaging with quantitative microwave holography (QMH). We show through examples with simulated and experimental data that in near-field imaging scenarios, including the imaging of a breast-tissue phantom, there are significant benefits in employing the new 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".