# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results
### Performance Evaluation of Finetuned Lightweight Network  
Our evaluation of the proposed lightweight dehazing network encompasses multiple aspects to provide a comprehensive understanding of its performance and efficiency. We focus on image quality, computational complexity, and processing speed to assess the effectiveness of our approach in achieving high-quality dehazing while maintaining low computational overhead. To implement progressive pruning, we conducted a series of experiments using a novel approach. Initially, we generated a pretrained model using the entire O-Hazy dataset. We then performed fine-tuning based on specific images, followed by importance-based pruning. This method allows us to tailor the model to particular scenes while reducing its size. The channel importance analysis results are visualized in Figure 9, which reveals significant variations in channel importance patterns when evaluating the pretrained model on different outdoor scenes and comparing these with scene-specific fine-tuned models. The analysis shows distinctive importance patterns when the pretrained model is evaluated on three different outdoor scenes (01, 02, and 03), as well as the patterns from models fine-tuned on each specific outdoor scene. The importance values demonstrate how different channels respond to and process various scene characteristics. For instance, we observe varying importance distributions across channels when the pretrained model is evaluated on different outdoor scenes, suggesting scene-dependent channel utilization. Additionally, the fine-tuned models show altered channel importance patterns compared to the pretrained model evaluations, indicating adaptation to specific scene characteristics during fine-tuning. However, our pruning experiments revealed that directly applying these importance scores for channel selection did not translate to optimal pruning decisions, as evidenced by the significant performance degradation in our pruned models. This discrepancy between channel importance analysis and pruning effectiveness highlights the complex relationship between channel importance and actual model performance, suggesting the need for more sophisticated pruning strategies that can better preserve the scene-specific optimizations achieved through fine-tuning. Figure 9: Channel importance comparison (Layer 7). Our research aimed to demonstrate the effectiveness of scene-specific fine-tuning in enhancing dehazing performance for a static scene. We hypothesized that models optimized for particular scenes would significantly outperform a generalized pretrained model. To test this hypothesis, we developed three fine-tuned models, each specialized for a specific image from the O-HAZY dataset. The comparative performance of our models reveals a clear pattern of scene-specific optimization. As visualized in Figure 10, each fine-tuned model consistently achieves the highest PSNR value on the image it was specifically trained on, while showing relatively lower performance on other images. For instance, the model finetuned on 01 achieves a PSNR of 17.74 dB when applied to 01_outdoor, outperforming both the pretrained model (14.65 dB) and the models finetuned on 02 and 03 (16.41 dB and 16.38 dB respectively) for this specific scene. Similarly, the model finetuned on 02 shows superior performance on 02_outdoor with a PSNR of 19.09 dB, compared to its performance on 01_outdoor and 03_outdoor (16.41 dB and 22.40 dB). This pattern is most pronounced for the model finetuned on 03, which achieves a remarkable PSNR of 25.16 dB on 03_outdoor, significantly higher than its performance on 01_outdoor and 02_outdoor (16.38 dB and 17.42 dB). However, our pruning experiments revealed significant limitations in maintaining this scene-specific performance advantage. The pruned models showed consistent degradation across all test images, with PSNR values dropping significantly below both the pretrained and finetuned models.