Compressive color imaging with group-sparsity on analysis prior
Why this work is in the frame
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Bibliographic record
Abstract
Compressed sensing (CS) of color images can be formulated as a group-sparsity promoting inverse problem. In the past, group-sparsity constraint was imposed on the CS synthesis prior formulation with an orthogonal transform to solve the inverse problem. The objective of this work is to empirically show that better results can be obtained if a group-sparsity constraint is imposed on the CS analysis prior formulation with a redundant transform. This problem requires solving a group-sparsity promoting inverse problem which has not been addressed earlier. Therefore we derive a new algorithm for solving it based on the Majorization-Minimization approach. Experimental results corroborate that analysis prior with a redundant transform gives far superior (about 1.5dB) improvement compared to synthesis prior with orthogonal transform.
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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 | 0.000 |
| 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