Bibliographic record
Abstract
Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".