A color reproduction method with image enhancement for endoscopic images
Bibliographic record
Abstract
This paper presents an efficient color image enhancement method for endoscopic images. The enhancement is achieved in two parts: image enhancement at gray level followed by a color reproduction. The captured RGB endoscopic images are first converted into three 2-D spectral images using a well-known method called FICE (Fuji Intelligent Color Enhancement). Then the image with the maximum entropy is selected as the base image to be used for color reproduction in the next stage. In color reproduction, the chrominance map of a source color image is added to the base image. This chrominance map is found by matching luminance and texture information between these two images based on neighborhood statistic method. The distance between luminance components of base gray image and source color images is calculated using 2-norm Euclidean distance. The proposed method highlights some of the tissue characteristics in the base endoscopic image which will enable better diagnosis. The performance of the scheme is compared with other related algorithms in terms of simulation speed, image quality, efficiency of color reproduction and distortion.
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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 | 0.001 |
| 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 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".