Bibliographic record
Abstract
We propose an efficient regularized restoration model associating a spatial and a frequential regularizer in order to better model the intrinsic properties of the original image to be recovered and to obtain a better restoration result. An adaptive and rescaling scheme is also proposed to balance the influence of these two different regularization constraints, preventing an overwhelming importance for one of them from prevailing over the other, enabling them to be efficiently fused during the iterative deconvolution process. This hybrid regularization approach, mixing these two constraints and, more precisely, favoring a solution image that is both efficiently denoised [due to the denoising ability of a thresholding procedure in the discrete cosine transform (DCT) domain] and edge-preserved [due to the generalized Gaussian Markov random field (GGMRF) constraint]; yields significant improvements in terms of image quality and higher signal-to-noise ratio improvement results compared to a single GGMRF or DCT prior model and leads to competitive restoration results in benchmark tests, for various levels of blur, blurred signal to noise ratio (BSNR), and noise degradations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".