Primal-dual algorithm for solving a convex image dejittering model with hybrid finite differences
Why this work is in the frame
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Bibliographic record
Abstract
Jittering is a common phenomenon arising from the area of multimedia data compression and wireless video transmission. The visual abnormality of a jittered image is the jag in edge and loss of synchronization in latitudinal direction. Typically, the problem of intrinsic image dejittering is challenging to be tackled because of the ubiquitous noise in jittered data. In this paper, we develop a convex variational model for solving image dejittering problem by exerting high-order finite differences regularizer in objective function and exploiting linearization to constraints. Upon the recent progress in convex optimization community, the proposed model can be efficiently solved by the first-order primal-dual algorithm. Numerical simulations on recovering both noiseless and noisy jittered data demonstrate the compelling performance of the proposed model.
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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