Brightness adjustment and contrast matching in low-light underwater images using feedforward neural networks
Bibliographic record
Abstract
Access to high-resolution underwater images is crucial for the conservation and development of marine resources. Light scattering and light absorption are two fundamental issues in improving the quality of underwater images. Many of the captured images have severe degradation, which harms the systems and activities that rely on these images. To address this problem, we introduce an auxiliary network to enhance the contrast of underwater images. This network consists of three critical components. In the first step, a decoder network is used to recover gradient maps and enhance the brightness of the images. Then, we take the help of a brightness adjustment network to control the brightness of the hidden image, and finally, we use an adaptive contrast module to adjust the contrast. To improve the performance, we use a normalizer module to solve the problem of not paying attention to the increase in image contrast when increasing the brightness. Evaluation of the proposed method with public dataset images shows that our method can increase the resolution of underwater images. In addition, the proposed model can increase the resolution of images in complex images, low-light, and dark conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".