A Fast Directional Sigma Filter for Noise Reduction in Digital TV Signals
Bibliographic record
Abstract
This paper proposes a structure-oriented multidirectional Sigma filter for additive white Gaussian noise in digital TV signals. Filtering is restricted to homogeneous directions to reduce blurring by analyzing local structure using directional second derivatives. The proposed filter improves the Sigma estimate of denoised pixels by imposing a homogeneity constraint on the noise-adaptive selection of estimation pixels by the Sigma filter. It achieves noise-reduction gains of up to 4.8 dB Peak-Signal-to-Noise Ratio (PSNR) in real-time. The block size, shape and coefficients of the filter are adapted to both structure and noise level. The goal is to optimize the filter with regard to noise-reduction gain and structure preservation. A possible hardware-oriented design of the proposed filter is also presented. To show the effectiveness of the proposed method, comparisons between the proposed Sigma filter and referenced Sigma filters in terms of the PSNR gain and the modulation transfer function (MTF) are shown. Results show that the proposed method achieves a higher PSNR gain and contrast transfer ratio than referenced Sigma filters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".