Image enhancement and space-variant color reproduction method for endoscopic images using adaptive sigmoid function
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 presents an image enhancement and space-variant color reproduction method based on adaptive sigmoid function for endoscopic image. At first, using YCBCR conversion matrix, the color image is separated into luminance and chrominance components. The adaptive sigmoid function with two controlling parameters is applied on the uniformly distributed luminance pixels. The space-variant color reproduction generates new chrominance components by transferring and modifying old chrominance based on texture information. Finally, new luminance and chrominance components are converted into RGB color image. The proposed method highlights some of the tissue and vascular characteristics as well as pit patterns in lesion and polyp. The performance of the proposed scheme is compared with other related methods in terms of image quality, focus value, efficiency of color reproduction and statistic of visual representation.
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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 | 0.001 | 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