<title>Cosine transform generalized to lie groups SU(2)xSU(2), O(5), and SU(2)xSU(2)xSU(2): application to digital image processing</title>
Why this work is in the frame
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Bibliographic record
Abstract
We propose to apply three of the multiple variants of the 2 and 3-dimensional of the cosine transform. We consider the Lie groups leading to square lattices, namely SU(2)xSU(2) and O(5) in the 2-dimensional space, and the cubic lattice SU(2)xSU(2)xSU(2) in the 3-dimensional space. We aim at evaluating the benefits of some Discrete Group Transform (DGT) techniques, in particular the Continuous Extension of the Discrete Cosine Transform (CEDCT), and at developing new techniques that refine image quality: this refinement is called the high-resolution process. This highest quality is useful to increase the effectiveness of standard features extraction, fusion and classification algorithms. All algorithms based on the 2 and 3-dimensional DGT have the advantage to give the exact value of the original data at the points of the grid lattice, and interpolate well the data values between the grid points. The quality of the interpolation is comparable with the most efficient data interpolation, which are currently used for purposes of image zooming. In our first application, we use DGT techniques to refine fully polarimetric radar images, and to increase the effectiveness of standard features extraction algorithms. In our second application, we apply DGT techniques on medical images extracted from a system and a Magnetic Resonance Imaging (MRI) system.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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