Removal of High Density Salt & Pepper Noise in Noisy color Images using Proposed Median Filter
Bibliographic record
Abstract
In this paper, we proposed removal of high density salt and pepper noise in noisy color images using purposed median filter. The performance of improved median filter is good at lower noise density level. The mean filter suppresses little noise and gets the worst results. The improved median filter is good at lower noise density levels. It removes most of the noises effectively while preserving colored image details. The proposed algorithm utilizes an impulse detector based on the threshold value obtained by un-symmetrical trimmed variants to check, if the pixel is noisy or not. If the pixels are found to be greater than the threshold then the corrupted pixel is replaced by midpoint of un-symmetrical trimmed values of current processing window, else left unaltered. The performance of the algorithm is analyzed in terms of Peak signal to noise ratio (PSNR), Mean square error (MSE), Image Enhancement Factor (IEF). The proposed algorithm is compared with standard and well known algorithms and found to have good noise removal capabilities with edge preservation. The performance of the algorithm is found good both quantitatively and qualitatively for very high noise densities.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".