2-D Kalman filtering for the restoration of stochastically blurred images
Why this work is in the frame
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Bibliographic record
Abstract
2D Kalman filtering for the restoration of stochastically blurred images is developed. Stochastic blur is treated as the combination of a deterministic blur and correlated random noise. For restoration from single-frame data, an augmented state-vector Kalman filter for stochastic blurs is derived. This filtering scheme is then extended to provide restoration from multiple-frame data also. Kalman filtering for both serial and parallel processing of the frames is proposed. The new filters can take into account the spatio-temporal correlations of the randomly varying blur. For the equivalent 1-D problem, the proposed filters are the best linear estimators for minimizing the mean-square error over the blur process ensemble and observation noise ensemble. Sample results are also provided to show the effectiveness of the proposed filters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 it