LiteDHAZE: An Adversarial Dehazing Network for Robust Robotic Perception in Challenging Visual Conditions
Bibliographic record
Abstract
Haze and fog severely degrade image quality, hindering reliable perception in robotic systems performing navigation, mapping, and object detection. We present LiteDHAZE, a lightweight generative adversarial network (GAN) for real-time single-image dehazing, leveraging edge-aware frequency decomposition and attention-guided enhancement. The architecture employs directional wavelet transform to extract high-frequency sub-band features and utilizes Res2Net-based multi-scale encoding to preserve structural details. A streamlined frequency-guided attention module reinforces both spatial and spectral feature relevance with minimal overhead. Unlike multi-branch frameworks, LiteDHAZE adopts a compact single-path encoder–decoder design that ensures low latency and strong generalization. Trained on the RESIDE dataset and evaluated using PSNR and SSIM, LiteDHAZE delivers competitive dehazing performance with superior efficiency, making it well-suited for embedded and real-time robotic vision systems.
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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 | 0.000 |
| 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".