Nonlinear Correction of the Direct Inverse Problem Solution in Real-Time Imaging
Bibliographic record
Abstract
This paper proposes a direct method for quantitative real-time imaging based on a nonlinear correction of the approximate linearized imaging kernel. The correction process relies on a pseudo-Rytov approximation employing the ratio between the total and incident fields in the scattering model, which can be estimated analytically. Unlike traditional iterative algorithms, there is no need for multiple computations of the direct scattering model, gaining computational speed and robustness to numerical inaccuracies. The procedure consists of two steps. First, a fast direct inversion algorithm based on the Born approximation provides the initial guess for the permittivity distribution; this study employs the Truncated Singular Value Decomposition (TSVD) and an in-house finite element-based solver to compute the imaging operator. Second, the field correction factor is transferred onto the object's permittivity to enhance its quantitative accuracy. The proposal viability is verified in 2D synthetic experiments at microwave frequencies, verifying improvements in the reconstructed unknown permittivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 0.001 |
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".