An iterative dynamic programming approach to 2-D phase unwrapping
Bibliographic record
Abstract
Abstract — We propose a novel Bayesian approach to 2-D phase unwrapping. Modeled as a first-order Gaussian Markov random field, the unwrapped phase is estimated according to a maximum a posteriori (MAP) rule. The estimate is made through a form of 2-D dynamic programming, using a series of row-by-row or column-by-column 1-D dynamic programming optimiza-tions. Increasing the number of states in the dynamic system can improve the unwrapping performance, but also increases the com-putational complexity. Due to this trade-off, a structured iterated conditional mode (SICM) is used to achieve good performance without examining a large number of states in each iteration. A row-by-row followed by column-by-column raster scan takes pre-vious estimates into account through a weighting. Other raster scans are also possible. The approach can be implemented effi-ciently in terms of memory usage due to the recyclable memory of dynamic programming. An example of the approach with seven states is given. Experimental results are compared to other algo-rithms including the least-squares method, the branch-cut method and Flynn’s method, using interferometric SAR data. The new SICM algorithm is seen to be superior. I.
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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".