A Mathematical Analysis and Implementation of Residual Interpolation Demosaicking Algorithms
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Bibliographic record
Abstract
Demosaicking is the process of reconstructing the full color image from its mosaic version on a Bayer pattern. It is an integral part of the image processing pipeline for single sensor digital color cameras. Demosaicking algorithms based on residual interpolation are interesting because they produce competitive results with a low computational complexity. In this article, we provide an analysis and careful implementation of the most relevant residual based demosaicking algorithms. Our contribution is twofold. First, we present an analysis of the mathematical principles of demosaicking algorithms from the Hamilton-Adams interpolation to the recent 'adaptive residual interpolation'. Our analysis untangles the relations of these algorithms and how each is improving on the preceding ones. Lastly, we provide a comparison between most recent state of the art methods on several image data sets and discuss their performances.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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