Image resolution up-conversion via maximum a posteriori interpolator sequence estimation and Viterbi algorithm
Why this work is in the frame
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Bibliographic record
Abstract
A new method of image resolution up-conversion based on maximum a posteriori sequence estimation is proposed. At each missing pixel of the high resolution (HR) image we consider an ensemble of candidate interpolation methods (interpolator). The interpolators are interpreted as states of a finite-state machine (FSM). Accordingly, the up-scaling problem is converted to the problem of estimating the optimal sequence of interpolators corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this FSM to solve the estimation problem using trellis diagrams and Viterbi algorithm. The experimental results prove that the proposed algorithm results sharper HR images and higher peak signal-to-noise ratios (PSNR) comparing to many algorithms in this domain.
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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 | 0.005 |
| 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