Bayesian Optimized GridDehazeNet for Adaptive Haze Removal in Real-World Applications
Haze removal is essential for improving image visibility in applications like autonomous vehicles and surveillance. While models like GridDehazeNet enhance dehazing performance, our research emphasizes that selecting appropriate image data and application methods is even more crucial. We propose an...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10955216/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Haze removal is essential for improving image visibility in applications like autonomous vehicles and surveillance. While models like GridDehazeNet enhance dehazing performance, our research emphasizes that selecting appropriate image data and application methods is even more crucial. We propose an adaptive haze removal system that integrates Bayesian Optimization with GridDehazeNet to automatically find the optimal network width and height for specific environments. Our experiments show that the optimal architectural parameters of the neural network—specifically, the height (h) and width (w) of the network architecture—vary for images captured by fixed-position cameras in real-world settings. By employing our algorithm, we effectively identify the best architectural parameters, achieving higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to models without this optimization. Notably, fine-tuning the model for specific environments yields a 36% average boost in PSNR over the baseline while reducing the model parameter count by 63%. This is a pivotal outcome, as it addresses two critical demands in dehazing: improved performance and computational efficiency. By demonstrating that a smaller, optimized network can surpass a larger, generic baseline in both accuracy and speed, our work suggests a practical pathway toward real-time dehazing solutions, particularly in resource-constrained or edge-based scenarios. |
|---|---|
| ISSN: | 2169-3536 |