# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results
### Performance for Static Scenes  
Our lightweight dehazing network demonstrates particularly impressive results when applied to fixed camera environments. This scenario is common in various applications such as traffic monitoring, outdoor surveillance, and weather stations, where cameras remain stationary and observe the same scene under varying atmospheric conditions. In fixed camera setups, our model leverages the consistent scene structure to achieve superior dehazing performance. We conducted extensive experiments using a dataset collected from fixed outdoor cameras over extended periods, capturing the same scenes under different haze conditions. The results show significant improvements in both dehazing quality and computational efficiency. Figure 12 provides a visual comparison of dehazing results for our original and pruned models on different images, illustrating the effectiveness of our approach in a static scene. Image Quality: In fixed camera scenarios, our fine-tuned models consistently outperform the original LightDehazeNet. For instance, when evaluated on a series of images from a single static viewpoint. Model f1 achieved an average PSNR improvement of 1.2 dB and SSIM increase of 0.05 compared to the original model. Models f2 and f3, fine-tuned on different static scenes, showed similar improvements, with PSNR gains of 1.1 dB and 1.3 dB respectively. These improvements are attributed to the model's ability to learn and adapt to the specific structural elements of the static scene, allowing for more accurate haze removal. The visual results in Figure 12 clearly demonstrate this enhanced dehazing quality across different scenes and models. Temporal Consistency: A key advantage in fixed camera environments is the improved temporal consistency of the dehazed video sequences. Our model maintains stable performance across consecutive frames, reducing flickering and temporal artifacts often observed in frame-by-frame dehazing approaches. Quantitative evaluation using temporal consistency metrics showed a 30% reduction in inter-frame fluctuations compared to the original model. Computational Efficiency: Our pruning experiments didn't show a significant reduction in processing time for this particular lightweight model as shown in Figure 6. However, it's important to note that the impact on inference time can be more pronounced in larger,  
Figure 12: Comparison of dehazing results for fine-tuned and pruned models on different images. more complex models. While our current lightweight model's inference time decreased only marginally with pruning, this approach could lead to substantial speed improvements in larger networks with millions of parameters. In our case, the fixed nature of the scene still allows for certain optimizations. By fine-tuning the model to specific scenes, we achieved better dehazing quality without increasing computational complexity, as evidenced by the improved PSNR and SSIM values of our fine-tuned models as seen in Figures 10 and 11. Inference Time Considerations: Although the reduction in inference time was not substantial in our lightweight model, it's crucial to consider the potential impact on larger networks. In more complex models, the reduction in parameters and MACs achieved through our pruning technique could translate to significant speed improvements. This scalability aspect of our approach makes it potentially valuable for a wide range of applications, from resource-constrained edge devices to more powerful systems handling multiple video streams. Adaptation to Lighting Changes: Our experiments also demonstrated the model's robustness to diurnal and seasonal lighting changes in static scenes. The fine-tuned models maintained consistent performance across different times of day and various weather conditions, showing only a marginal decrease in PSNR (0.3 dB) under extreme lighting variations. Real-world Application: We deployed our lightweight model in a real-world traffic monitoring system for an extended period.