Dimension adaptive hybrid recovery with collaborative group sparse representation based compressive sensing for colour images
Bibliographic record
Abstract
In this paper, a fast and efficient hybrid method of image compressive sensing (termed as HRCoGSR) is designed which can adaptively acquire grey or colour image and can faithfully recover it speedily.The proposed method combines and utilises the approaches of recovery via collaborative sparsity (RCoS) and group sparse representation (GSR).For fast convergence, Gaussian Pyramid (GP) is constructed at the front-end and then block compressive sensing (BCS) based RCoS recovery is applied.In the second phase, restricted GSR process is carried out for further enhancing the perceptual quality.The collaborative sparsity-based CS solution is an iterative method and intends to improve signal-to-noise ratio (SNR) performance of the recovered image.It simultaneously enforces local 2D and 3D non-local sparsity in adaptive hybrid transform domain.Parametric performance of the proposed HRCoGSR method is tested over variety of standard grey and colour images and compared with seven existing state-of-the-art methods.Experimental results show that the proposed HRCoGSR method is highly efficient and much faster than existing methods.The average computational time taken by the proposed method is only 26% of that of the standard RCoS method and 46% of the GSR method.
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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 | 0.001 | 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".