Specification of the observation model for regularized image up-sampling
Bibliographic record
Abstract
Regularization is one of the most promising methods for image up-sampling, which is an ill-posed inverse problem. A key element of such a regularization approach is the observation model relating the observed lower resolution (LR) image to the desired higher resolution (HR) up-sampled image, used in the data-fidelity term of the regularization cost function. This paper presents an algorithm to determine this observation model based on a model of the physical acquisition process for the LR image, and the ideal acquisition process for the desired HR image, both from the same underlying continuous image. The method is illustrated with typical scenarios corresponding to LR and HR cameras modeled by either Gaussian or rectangular apertures. Experiments with some regularized image up-samplers demonstrate the importance of using the correct, adapted observation model as determined by our algorithm. Index Terms-Camera aperture, data fidelity, image up-sampling, interpolation, multidimensional signal processing, observation model, power spectral density (PSD), super-resolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".