Adaptive video filtering framework
Bibliographic record
Abstract
Abstract We present a new 3D adaptive filtering approach capable of detecting and removing impulsive noise in image/video sequences. The proposed method takes advantage of switching median schemes and robust lower‐upper‐middle (LUM) smoothing characteristics. Simulation studies reported in this article indicate that the proposed filtering scheme achieves an excellent trade‐off between noise attenuation and detail preserving characteristics, and clearly outperforms previously introduced approaches in terms of subjective and objective image quality measures. Besides the filter analysis and the testing of its performance, an important part of this article discusses the filter implementation in Altera field programmable logic devices (FPLD). Simulation studies indicate that the proposed method can be efficiently implemented in hardware and is suitable for real‐time image/video processing applications. © 2005 Wiley Periodicals, Inc. J Imaging Syst Technol 14, 223–237, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20027
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".