Hybrid Approach to Detect Fog Level Using CNN and Defog the Video Sequence Using GAN
Bibliographic record
Abstract
Fog significantly reduces video clarity by lowering contrast, blurring objects, and distorting colors hampers scene understanding in critical applications such as autonomous driving, traffic surveillance, and remote sensing.Existing defogging methods like Retinex-based algorithms and dark channel prior often fail under varying fog densities and lack real-time adaptability, leading to detail loss or visual artifacts.To address these challenges, this study introduces a hybrid deep learning approach that integrates a Convolutional Neural Network (CNN) for fog level detection and a Generative Adversarial Network (GAN) for adaptive video defogging.The CNN accurately estimates fog density per frame, enabling the GAN to adjust its processing and preserve essential visual features.Preprocessing steps such as grayscale conversion and histogram equalization, enhance feature extraction and improve defogging performance.The system is designed for real-time deployment and adaptability across different atmospheric conditions.Evaluation using metrics such as PSNR, SSIM, FADE, NIQE, MAE, and RMSE demonstrates superior performance compared to existing and state-of-the-art methods like MSBDN and FFA-Net.Notably, under heavy fog, the proposed model achieved a PSNR of 26.0 dB, SSIM of 0.86, FADE score of 0.41, and runtime of 0.31 s/frame, confirming its efficiency and suitability for safety-critical, lowvisibility environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".