Multi-Objective Threshold Optimized Image De-Noising Algorithm for High Density Mixed Impulse Noise
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Bibliographic record
Abstract
This paper proposes a novel Multi-Objective Optimization based Fuzzy Switching Median Filter (MOOFASMF) to remove high density Random Valued Impulse Noise (RVIN), “Salt & Pepper” Impulse Noise (SPIN) and Mixed Impulse Noise (MIN). In this work, multi-objective optimization technique is used to find out the fuzzy switching median filter threshold values for accurate detection of corrupted pixels. The proposed multi-objective framework uses Decomposition based Multi Objective Evolutionary Algorithm (MOEA/D) to obtain optimized fuzzy switching median filter drives the threshold values with the objectives Mean Square Error (MSE) and inverse of Structural Similarity Index Metrics (SSIM) as optimization objectives. Even though the MSE and SSIM are not closely related parameters, the optimized threshold value gives better results in terms of both PSNR and SSIM. The advantages of the proposed framework are that it works effectively on RVIN, SPIN, and MIN-affected images. The effectiveness of the proposed framework is outstanding for high-density RVIN, SPIN, and MIN, which makes it more advantageous over other existing methods. Experimental results in terms of visual and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Metrics (SSIM), and Edge Preservation Index (EPI) clearly demonstrates the better performance of the proposed algorithm over the state of art techniques. The proposed framework performed 6.02% and 32.11% better than the best existing methods in terms of PSNR and SSIM for the mixture of 40% SPIN & 50% RVIN affected image.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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