Image interpolation using a simple Gibbs random field model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial interpolation is an important technique that is often used to recover an image from its downsampled version, or to simply perform image expansion. Many conventional linear techniques exist, however, these often perform rather poorly in a subjective manner. In this paper, image interpolation is performed using a binary-based Gibbs random field (GRF) model. Images are interpolated from their downsampled versions along with a number of texture parameters that are estimated within smaller image blocks. These iterative GRF methods are subsequently approximated by a non-iterative nonlinear filtering operation, thereby reducing the computational complexity of the interpolation process. Experimental results indicate that the statistical GRF approaches adapt to textured regions as well as the smooth areas within an image, and thus, can achieve better results than the conventional linear schemes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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