# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results
### Analysis of Progressive Pruning for Light DeHaze Networks  
Our progressive pruning approach for Light DeHaze Networks revealed interesting insights into the trade-off between model compression and dehazing performance. Figure 13 illustrates the results of our pruning experiments, focusing on the layer 7 of our model.  
As shown in Figure 13, we plotted the PSNR values against different pruning ratios, starting from 0 to 0.30 in increments of 0.05. The graph displays how the PSNR changes as we progressively prune more channels from the layer 7. We set an original PSNR of 20.73 dB and a target PSNR of 18.00 dB, represented by horizontal lines on the graph.  
The results reveal several interesting patterns in the pruning process. At the initial stages, with pruning ratios of 0.00 and 0.05, the PSNR remained constant at 20.73 dB. This  
Figure 13: Progressive pruning results.  
stability indicates that the model could maintain its performance even with slight pruning, suggesting some level of redundancy in the original network.  
As we increased the pruning ratio from 0.10 to 0.25, we observed a performance plateau. The PSNR stabilized around 18.17-18.18 dB, remaining consistently above our target threshold of 18.00 dB. This plateau is particularly interesting as it suggests that the model found a balance, maintaining acceptable performance while significantly reducing its size. During this phase, we were able to reduce the number of channels in the layer 7 from 32 to 24, achieving a 25% reduction in the layer's size without dropping below our performance target.  
However, the pruning process revealed a critical point at a pruning ratio of 0.30. At this point, we observed a sharp drop in PSNR to 15.97 dB, falling below our target threshold. This sudden decrease in performance marks the point where pruning began to significantly impact the model's ability to effectively dehaze images.  
Based on these results, we identified the optimal pruning ratio as 0.25. At this point, the model achieved a PSNR of 18.17 dB, just above our target threshold, while maximizing the reduction in model size. Importantly, at this optimal pruning ratio, the model also maintained an SSIM of 0.7174, indicating good preservation of structural similarity despite the pruning.  
Our progressive pruning strategy, informed by these results, successfully achieved a balance between model size reduction and performance retention. By stopping at the 0.25 pruning ratio, we managed to reduce the model size by 25% while maintaining a PSNR above our target threshold. This approach proves particularly effective in fixed camera scenarios, where the pruned models can be optimized for specific scenes.  
The results suggest that our progressive pruning method offers a promising solution for deploying efficient dehazing models in resource-constrained environments, especially for static monitoring systems. It allows us to find the optimal point where we can maximize model compression without sacrificing too much dehazing quality. This balance is crucial for real-world applications where both computational efficiency and image quality are important considerations.