PAIR : Perception Aided Image Restoration for Natural Driving Conditions
Bibliographic record
Abstract
We present a two-stage mechanism for generic image restoration in natural driving conditions, where multiple non-linear degradations simultaneously impact perception for humans and driving assistance systems. Our approach overcomes the limitations of utilizing a single neural network that incurs excessive computational overhead and yields sub-optimal recovery. The proposed first stage comprises computationally inexpensive image processing operations applied at a patch level using a lightweight convolutional neural network (CNN) that determines their intensity of operation. This patch size is guided by the receptive field of the CNN, allowing for dynamic restoration of non-linear and non-homogeneous degradation profiles. The second stage leverages a lightweight end-to-end neural network functioning as an inpainting network. It identifies inadequately restored regions and leverages global semantic and structural information to fill the affected areas. This approach enhances the restoration process by considering the entire image and addresses the remainder of localized deficiencies. In addition, we integrate dense perception tasks such as semantic and depth estimation during the optimization cycle to ensure restored images that are perceptually pleasing and conducive for downstream perception tasks. Since datasets covering diverse degradation scenarios for high- and low-level perception tasks are lacking, we utilize a synthetic data augmentation technique to generate non-homogeneous non-linear degradation profiles. Experiments on images captured in adverse weather conditions demonstrate the efficacy of our approach, yielding higher perceptual quality in restored images and improved performance in downstream perception tasks under adverse driving conditions. Importantly, our method offers computational efficiency compared to end-to-end image restoration algorithms, making it suitable for real-time applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".