Data Efficient Single Image Dehazing via Adversarial Auto-Augmentation and extended Atmospheric Scattering Model
Bibliographic record
Abstract
Supervised learning-based image dehazing algorithms are sensitive to degradation and training distribution, making them ill-suited for out-of-domain non-uniform restoration. We propose an adversarial auto-augmentation approach to address this limitation without explicitly collecting paired training data. Specifically, we generate images with a broad distribution representative of multiple domains by varying the degradation and color profiles achieved by leveraging new augmentation techniques, including mean-variance transfer, physically accurate atmospheric scattering model, and localized degradation generation. These techniques effectively account for non-homogeneous degradations, enhancing the robustness of the underlying degradation model. Apart from utilizing these synthetic negative images to train the underlying network, these also provide diverse image representations for enabling more effective contrastive regularization. In addition to the training modifications, we propose a frequency-based feature fusion mechanism that prioritizes semantic and structural information from the decoder and encoder. Finally, we incorporate depth and color attenuation priors to ensure perceptually pleasing and physically accurate restoration quality. To evaluate the efficacy of the proposed mechanism, we perform comprehensive experiments and obtain state-of-the-art (SoTA) results while achieving high fidelity and improving the performance of perception-based algorithms without fine tuning.
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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 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".