Comparison of Reducing the Speckle Noise in Ultrasound Medical Images using Discrete Wavelet Transform
Why this work is in the frame
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Bibliographic record
Abstract
Speckle noise in ultrasound (US) medical images is the prime factor that undermines its full utilization. This noise is added by the constructive / destructive interference of sound waves travelling through hard- and soft-tissues of a patient. It is therefore generally accepted that the noise is unavoidable. As an alternate researchers have proposed several algorithms to somewhat undermine the effect of speckle noise. The discrete wavelet transform (DWT) has been used by several researchers. However, the performance of only a few transforms has been demonstrated. This paper provides a comparison of several DWT. The algorithm comprises of a pre-processing stage using Wiener filter, and a post-processing stage using Median filter. The processed image is compared with the original image on four metrics: two are based on full-reference (FR) image quality assessment (IQA), and the remaining two are based on no-reference (NR) IQA metrics. The FR-IQA are peak signal-to-noise ratio (PSNR) and mean structurally similarity index measure (MSSIM). The two NR-IQA techniques are blind pseudo-reference image (BPRI), and blind multiple pseudo-reference images (BMPRI). It has been demonstrated that some of these wavelet transforms outperform others by a significant margin.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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