Bibliographic record
Abstract
In order to remove Gaussian noise and impulse noise from video data,a nonlocal video denoising algorithm based on S1/2 matrix norm is proposed.Firstly,using a diamond search algorithm for fast patch-matching,some patches similar to the given reference patch are found and collected in the video data.Secondly,all of the columns of similarity patches are recombined to form a new matrix and the new matrix is decomposed into a low rank matrix and a sparse matrix based on S1/2 matrix norm.The low rank matrix represents the scene information data of the original video and the sparse matrix represents the impulse noise data and outliers existing in the noisy video.Lastly,the estimated values of a denoised reference patch are determined by taking the weighted average of all the data recovered from the low rank matrix,and the estimated values of the denoised frame are got based on the combinations of all the recovered reference patches in a frame.Experimental results show that the proposed scheme can effectively remove Gaussian noise and impulse noise from the video.Compared with two existing state-of-art algorithms,the proposed algorithm has noticeable superiority in both visual effect and objective evaluation.
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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 | 0.002 |
| 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".