Bibliographic record
Abstract
In spite of the availability of advanced imaging devices with high sensitivity, high resolution, and built-in image-data processing procedures, images are often acquired with quality that is unsatisfactory or inadequate for certain purposes. When considering methods to modify such images with the aim of enhancing their quality, it is important to recognize and understand the several notions and factors that affect and determine the quality of an image; see Sections 2.2 and 2.3 as well as Rangayyan [6]. If further analysis of the processed image is to be performed by a human observer, the subjective and qualitative nature of such analysis needs to be taken into consideration. On the other hand, if subsequent analysis of the image is relegated to yet another computational procedure, the objective or quantitative requirements of the procedure should be taken into account in the design of the enhancement procedure. Thus, the nature and extent of enhancement to be effected on an image depend upon further use of the processed image. In most cases, the enhancement sought in an image would be aimed to achieve one or more of the following desired characteristics: • uniform or balanced brightness across the image, which may require dark areas to be made lighter and areas of excessive brightness to be made less bright; • good contrast and visibility of detail; • sharp and well-defined edges and borders of objects or regions in the image; • clean and clear representation of the original objects or scene with no noise or blemishes; • faithful reproduction of hues or shades of color, with particular attention to skin tone and hue in images of humans; and • good color balance to result in a pleasant appearance. Several digital image-processing techniques have been proposed to address the requirements stated above in the case of grayscale images [1, 6]. However, the extension of techniques designed for grayscale or monochromatic images to process color or vector images is neither straightforward nor always appropriate; the methods described in Chapter 3 to remove noise in color images illustrate some related concepts and methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".