# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 4 Results  
### Experimental Setup  
Our experiments were conducted using the O-HAZY dataset, a comprehensive collection of outdoor hazy images paired with their corresponding ground truth clear images. This dataset provides a realistic and challenging benchmark for dehazing algorithms, offering a diverse range of haze densities and atmospheric conditions. The O-HAZY dataset comprises 45 pairs of outdoor hazy images and their corresponding clear ground truth images. These images cover a variety of scenes, including urban environments, natural landscapes, and complex structural settings. The dataset captures images under different atmospheric conditions, haze densities, and lighting situations. This diversity ensures that the dataset is representative of real-world scenarios, providing a robust foundation for training and evaluating dehazing algorithms. Utilizing the O-HAZY dataset allows us to pretrain our model on a wide range of conditions, ensuring that it learns generalizable dehazing features. This is crucial for our approach, as it enables the model to be effectively fine-tuned for specific static scenes while maintaining robust performance across varying haze conditions.  
We specifically focused on three key images from the dataset. These images were selected to represent different levels of haze intensity and scene complexity, allowing us to evaluate our model's performance across a spectrum of dehazing challenges. Figure 3 illustrates the network architecture of our 10% and 60% pruned models for image dehazing before lightweighting, providing a visual representation of our model structure.  
To assess the performance of our proposed lightweight dehazing network, we employed several evaluation metrics. The primary metrics used were Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). PSNR is defined as:  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right) \tag{2}\]  
where \(MAX_{I}\) is the maximum possible pixel value of the image and MSE is the mean squared error between the dehazed image and the ground truth. Higher PSNR values indicate better image quality.  
SSIM is calculated as:  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{ 2}+\mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{3}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the average of \(x\) and \(y\) respectively, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variance of \(x\) and \(y\) respectively, \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), and \(c_{1}\) and \(c_{2}\) are variables to stabilize the division with weak denominator. SSIM values range from \(-1\) to \(1\), with higher values indicating greater structural similarity to the ground truth.  
In addition to these image quality metrics, we also measured the computational complexity of our models in terms of the number of parameters and Multiply-Accumulate Operations (MACs), as well as the inference time required to process a single image. These  
Figure 3: The 10% and 60% prune network model for image dehazing before lightweighting.  
metrics are crucial for assessing the lightweightness and real-time performance capabilities of our approach.