# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results
### Performance Evaluation of Finetuned Lightweight Network
For example, after pruning, the model pruned on 01 showed a substantial decrease in performance, with PSNR dropping to 11.89 dB on its specifically trained image, and even lower values of 9.92 dB and 10.57 dB on 02_outdoor and 03_outdoor, respectively. Similar performance degradation patterns were observed across all pruned models, suggesting that our current pruning strategy, while effective in reducing model size, significantly compromises the network's ability to maintain high-quality dehazing performance, particularly for scene-specific optimizations. Figure 11 further emphasizes these improvements by illustrating the PSNR differences between finetuned and pretrained models. This visualization clearly shows positive PSNR gains for each model on its respective training image, with the most substantial improvements observed when models are applied to their specific training scenes. These results consistently demonstrate the effectiveness of our scene-specific fine-tuning approach, highlighting its potential for enhancing dehazing performance in fixed camera scenarios. Interestingly, while our fine-tuned models showed the most significant improvements on their specific training images, they also maintained competitive performance across other test images. This observation suggests that our fine-tuning approach not only enhances scene-specific dehazing but also retains a degree of generalization capability. Such a balance is crucial for practical deployments where lighting conditions and haze density may vary even within a static scene. The comparative performance of our models is visualized in Figure 10, which presents PSNR values across different models and test images. Figure 11 further emphasizes the improvements by illustrating the PSNR differences between fine-tuned and pretrained models. These visualizations clearly demonstrate the consistent superiority of scene-specific fine-tuning, particularly when models are applied to their respective training scenes. Our findings have significant implications for the development of efficient, high-performance dehazing systems in fixed camera environments. By leveraging scene-specific characteristics, we can achieve substantial improvements in dehazing quality without increasing model complexity. This approach paves the way for more effective and resource-efficient dehazing solutions in various real-world monitoring and surveillance applications. The visual results of our approach are presented in Figure 12. This figure provides a side-by-side comparison of dehazing results for both original and pruned models across different images. The top row shows results for image 01, the middle row for image 02, and the bottom row for image 03. In each row, we present results from models f1, f2, and f3 (from left to right) for both original and pruned versions. These visual results corroborate our quantitative findings. The fine-tuned models consistently produce clearer, more detailed images compared to the pretrained model, especially on their respective training images. For instance, model f1 shows superior performance on image 01, with improved contrast and detail preservation. Similarly, models f2 and f3 excel on images 02 and 03, respectively, demonstrating the effectiveness of our fine-tuning approach in adapting to specific scene characteristics. Interestingly, while pruning generally leads to some degradation in image quality, our importance-based pruning strategy helps mitigate this effect. The pruned models, though slightly less effective than their unpruned counterparts, still maintain a high level of dehazing performance, often surpassing the pretrained model. This balance between model size reduction and performance retention is a key strength of our proposed approach, making it particularly suitable for deployment in resource-constrained environments. Figure 11: Differences in PSNR (finetuned – pretrained). Figure 10: PSNR comparison.