Biomedical image denoising using variational mode decomposition
Bibliographic record
Abstract
This paper compares three biomedical image denoising techniques based on the recently introduced variational mode decomposition (VMD), the empirical mode decomposition (EMD), and the well-known discrete wavelet transform (DWT). The work focuses on using the VMD lowest mode or the EMD residue for denoising images corrupted with Gaussian noise, as opposed to DWT decomposition with thresholding. The comparison is made on a data set composed of a brain magnetic resonance image (MRI), a prostate tissue image, and a retina digital image. Based on peak-signal-to-noise ratio (PSNR), the results show that the VMD and EMD approaches outperform the conventional DWT-based thresholding approach, and that the VMD performed best overall. It is concluded that biomedical image denoising based on the VMD lowest mode or the EMD residue is a promising approach in comparison to DWT thresholding.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".