Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration
Bibliographic record
Abstract
Low-light image enhancement is a branch of digital image processing and is of great importance in many applications: aerial images, tracking, medical imaging and other applications.Therefore, this study aims to enhance the very low light images by relying on contrast limited adaptive histogram equalisation (CLAHE) with non-leaner sigmoid mapping based on YIQ colour space.Thus, the lighting component (Y) has been enhanced using CLAHE and sigmoid mapping, and the colour compound (IQ) has been treated via colour restoration.Color conversions are used to reduce any color error after enhancement, as the lighting component can be processed using CLAHE from the lighting component only.Two quality measures, namely, perception-based image quality evaluator (PIQE) and entropy (EN) were adopted to determine the efficiency of improvement.The proposed method was compared with several other methods, and the results were analysed.The findings indicate that the proposed method increased the lighting and restored the colours; it obtained the highest values for PIQE (34.598) and EN (7.025).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".