A Fast Directional Sigma Filter for Noise Reduction in Digital TV Signals
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it