A New Method for Impulse Noise Elimination and Edge Preservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a new method for impulsive noise reduction and edge preservation in images is presented. Images of different characteristics corrupted with a wide range of impulsive noise densities using two impulsive noise models are examined using the proposed method. In the detection stage of the method, two conditions have to be met to determine whether an image pixel is noisy or not. Two predetermined threshold values are involved in the computation of the second condition to differentiate between corrupted and uncorrupted pixels. Only pixels determined to be noisy in the detection stage are filtered in the next filtering stage where small size sliding windows are used to significantly reduce blurring effects in the output restored images. Several measuring indices have been used to examine the performance of the proposed method compared with many existing state-of-the-art methods in the literature of the image restoration field. Extensive simulation results show the superior performance of the proposed method over other techniques in terms of restoration quality, and preservation of images with fine details and edges.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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