Research on Underground Non-uniform Fog Removal Method Based on Enhanced Parallel Attention Mechanism
Bibliographic record
Abstract
The image quality in the underground environment is limited by insufficient lighting and the interference of non-uniform dust and mist generated by work activities. This non-uniform fog results in low image visibility, blurry details, and color distortion, which hinders underground safety monitoring. For this purpose, a model was designed for the removal of non-uniform fog underground. Firstly, the module includes multi-scale convolution and parallel attention mechanism. Multi scale convolution can obtain more feature information from images in order to restore texture information. Parallel attention can better capture multi-dimensional global information, improve the comprehensiveness of feature extraction, and perform well in removing non-uniform fog. In addition, the SE attention module is introduced to automatically learn the sensitivity of different channels to fog concentration, with high weights for dense fog areas, enhancing the dehazing effect. Finally, the PSNR and SSIM of the Haze4K dataset were verified to be 32.18 and 0.963, respectively. The validation indicators for the self-made non-uniform fog dataset are PSNR of 32.37dB and SSIM of 0.981. This provides a certain reference value for obtaining high-quality images for underground monitoring.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".