Wavelet-based texture-characteristic morphological component analysis for colour image enhancement
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel colour image enhancement method which uses wavelet-based texture characteristic morphological component analysis (WT-TC-MCA) to enhance the textural differences in the luminance channel of the colour image. The image enhancement method is intended to be the preprocessing method prior to the use of the colour image segmentation. The input colour image is firstly transformed to CIELab colour space to separate the luminance channel from the chromatic channels. Then only the luminance channel is enhanced by the WT-TC-MCA method to enhance the textural differences between different textures. Therefore, the colour image is enhanced with more differentiate textures while preserving the chromatic information. The experimental results show that the proposed method can enhance different colour image segmentation algorithms more than the state-of-the-art colour image enhancement method.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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