Improvement of Recovery in Segmentation-Based Parallel Compressive Sensing
Bibliographic record
Abstract
This paper extends the recently introduced 1-D Kronecker-based Compressive Sensing (CS) recovery technique to 2-D signals and images. Traditionally large sensing matrices are used while compressing images using CS. CS when applied to individual columns of the image instead of the entire image during the sensing phase, leads to smaller sensing matrices and reduction in computational complexity. For achieving further reduction in computational complexity, the column vectors are further segmented into smaller length segments and CS is applied to each of the smaller length segments. This segmentation process reduces quality of the recovered signal. To enhance the quality of the recovered signal, the entire column vector is recovered using the Kronecker-based CS recovery technique. Magnetic Resonance (MR) images from NCIGT database were used to demonstrate the superiority of the Kronecker-based recovery for 2-D images. Structural similarity and reconstruction error were used to compare the results obtained from Kronecker-based recovery technique with non-Kronecker-based repeated recovery applied to each segments individually. Kronecker-based recovery showed improvement over non-Kronecker-based individual recovery even at higher CR.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".