LiteDHAZE: An Adversarial Dehazing Network for Robust Robotic Perception in Challenging Visual Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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 it