Computationally fast techniques to reduce AWGN and speckle in videos
Bibliographic record
Abstract
Fast schemes to reduce additive white Gaussian noise (AWGN) and speckle in videos are presented. The proposed schemes use a change detection technique to measure the interframe motion and carry out estimations in both the spatial and temporal directions of the video. In the case of AWGN reduction, the well-known edge adaptive Wiener filter is used to perform the spatial estimation. Two different filters to carry out temporal estimation are presented based on novel weighted scalar Kalman and weighted running average filters, respectively. These temporal estimators are applied on the spatial estimate to obtain the spatiotemporal estimate. A new method is then used to appropriately combine the spatial and spatiotemporal estimates in order to obtain the final estimate of the uncorrupted signal. To achieve speckle reduction, we use an unbiased homomorphic system that comprises an edge adaptive filter for spatial estimation and the weighted running average filter for temporal estimation. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations. It is found that the use of a change detection technique, instead of the popularly used complex motion estimation and compensation technique, to measure the interframe motion results in a considerable reduction of processing time. The proposed schemes perform equally well or better than the existing schemes in reducing the noise in videos.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".