Adaptive order statistic filters: the complexity/quality tradeoff
Bibliographic record
Abstract
The authors compare, in some detail, six adaptive order-statistic-based filters with the median filter for image processing purposes. The most commonly used order-statistic filter is the median filter since it is easy to implement and removes impulse noise while preserving edges. One problem of the median filter is that its fixed window size constrains its performance. A large window size will give good impulse noise suppression but may blur the image while a small window size may not adequately remove the noise. Another problem is that the median filter is not the optimum filter for removing Gaussian noise. Each of the six adaptive order-statistic filters examined attempts to solve one or both of these problems, with the tradeoff being increased computational complexity for better image quality. When choosing a filter one must look at the computational complexity, the type of noise to be removed, the image quality required, and what kind of prior knowledge is required by the filters. The seven filters are examined for a variety of images and noise types. Some image quality results are presented.>
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".