# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results
### Performance Analysis  
To evaluate the effects of pruning on our model, we conducted experiments with pruning ratios ranging from 0 to 0.8. As expected, we observed a significant reduction in model complexity as the pruning ratio increased. The number of MACs decreased from approximately 1.99 billion for the unpruned model to 1.47 billion at an 80% pruning ratio, representing a 26% reduction as shown in Figure 4. Similarly, the number of parameters dropped from 30,187 to 22,287, a 26.2% decrease. This reduction in model size and computational requirements is a key advantage of our pruning approach, potentially allowing for deployment on more resource-constrained devices. Interestingly, while we anticipated that reduced model size would lead to faster training and inference times, our results showed a more nuanced picture. The training time per epoch remained relatively consistent across different pruning ratios, ranging from 0.55 to 0.84 s (Figure 5). This suggests that other factors, such as data loading and optimization algorithms, may have a more significant impact on training time than model size alone. Figure 4: Model Size vs. Pruning Ratio. Figure 5: Total training time for different pruning rates. Each ratio is represented by ten dots corresponding to training times. Similarly, the inference time did not decrease as dramatically as we initially expected. For the unpruned model, inference time averaged around 0.25 s per image, while the most heavily pruned model (80% pruning ratio) still required about 0.20 s as shown in Figure 6. This relatively modest 20% reduction in inference time, despite the significant decrease in model size, indicates that other bottlenecks in the inference pipeline may be limiting the speed improvements. As for the impact on model performance, we observed a clear trade-off between model size and dehazing quality. The final loss after training increased from 0.0187 for the unpruned model to 0.0216 at 80% pruning as shown in Figure 7, indicating a decrease in the model's ability to fit the training data. This trend was further reflected in the PSNR and SSIM metrics as shown in Figure 8. The average PSNR across our test images dropped from about 17.14 dB for the unpruned model to 16.58 dB at 80% pruning, while SSIM decreased from 0.7067 to 0.6790. These results highlight the complex relationship between model pruning and overall performance. While pruning effectively reduces model size and computational require  
Figure 6: Inference time for different pruning rates. Each ratio is represented by ten dots corresponding to inference times. Figure 7: Final loss for different pruning rates. Each ratio is represented by ten dots corresponding to final losses. ments, it also impacts the model's ability to perform the dehazing task. This underscores the need for a more progressive pruning strategy that can balance the benefits of a lightweight model with the maintenance of dehazing quality, especially in the context of fixed camera environments where consistent performance is crucial. For our pruning experiments, we calculated the importance of each channel based on its impact on the PSNR and SSIM when removed. This channel importance metric guided our pruning decisions in the proposed importance-based channel pruning technique. The channel importance comparison for layer 7 is visualized in Figure 9, providing insights into how different channels contribute to the model's performance. To evaluate the effectiveness of our proposed approach, we established several baseline models. Our primary baseline is the original LightDehazeNet, representing the state-of-the-art in lightweight dehazing networks prior to our modifications. We also created three fine-tuned versions of our network, labeled as Model f1, f2, and f3, each fine-tuned on one of the three key images. These models help us evaluate the effectiveness of our fine-tuning strategy for fixed camera environments.