Biomedical image denoising using variational mode decomposition
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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