A new wavelet-based method for contrast/edge enhancement
Bibliographic record
Abstract
Contrast enhancement is usually achieved by histogram equalizing image pixel gray-levels in the spatial domain to redistribute them uniformly. Meanwhile, edge enhancement attempts to emphasize the fine details in the original image. But in the spatial domain it is hard to selectively enhance details at different scales. Moreover, in the spatial domain, applying contrast and edge enhancement techniques in different orders may yield different enhancement results. To overcome the above spatial domain enhancement issues, a new wavelet-based image enhancement method is proposed. The proposed method histogram-equalizes the approximation-coefficients. At the same time, it high-boost filters the detail-coefficients at selected resolution levels separately. The experiments show that utilizing the proposed method can achieve robust contrast and edge enhancement. Moreover, the computation cost in the wavelet domain is less than that in the spatial domain. This is especially true when considering that currently most images are already wavelet-compressed (the current JPEG 2000 standard is a wavelet based scheme).
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".