# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 3 Methods
### Pretraining and Fine-Tuning Strategies  
The foundation of our progressive lightweight dehazing system is built upon a comprehensive pretraining process utilizing a massive, diverse dataset. This dataset, meticulously curated, encompasses a wide array of scenes, ranging from urban landscapes to natural environments, each captured under varying haze conditions and lighting situations. The  
Figure 2: Base Model for Dehazing. The three dots (...) under the last Conv box on the left indicate that the network architecture continues and connects to the right side of the figure. The diagram is split into two parts for better visualization.  
diversity in our training data is crucial, as it enables the model to learn generalizable features that are essential for effective haze removal across different scenarios.  
Our network architecture, illustrated in Figure 2, is carefully designed to balance complexity and efficiency. The lightweight model consists of a series of convolutional layers, each followed by ReLU activation functions. The network begins with an input layer accepting 3-channel images of size 256 \(\times\) 256. It then proceeds through multiple convolutional stages, with each stage increasing the feature depth while maintaining spatial dimensions.  
A key feature of our architecture is the use of concatenation operations that combine features from different levels. This design choice allows the network to utilize both low-level and high-level information, crucial for effective haze removal. Additionally, we implement a skip connection that carries the input directly to the final stages, facilitating the learning of residual information. This approach helps in preserving important details throughout the dehazing process.  
The final stages of our network involve element-wise multiplication and subtraction operations, followed by an addition. These operations play a critical role in the dehazing process, allowing the network to refine and adjust the image features for optimal haze removal. This structure enables our model to effectively capture and process the complex features necessary for haze removal while maintaining a relatively low computational overhead.  
We employ this deep convolutional neural network structure, incorporating these carefully designed connections to facilitate the learning of both low-level features and high-level semantic information relevant to the dehazing task. The use of concatenation and skip connections helps in preserving important information throughout the network, which is essential for high-quality image restoration. The choice of this architecture is motivated by its proven capability to handle the intricacies of image restoration tasks while maintaining computational efficiency, making it well-suited for our progressive lightweight dehazing system.  
The pretraining process itself is a critical phase in our system's development. We utilize a combination of perceptual and reconstruction losses to guide the learning process. The perceptual loss ensures that the dehazed images maintain natural visual qualities, while the reconstruction loss focuses on pixel-level accuracy. Our optimization strategy employs the Adam optimizer with a carefully scheduled learning rate decay to ensure stable convergence. To further enhance the model's robustness, we implement extensive data augmentation techniques, including random cropping, flipping, and subtle color jittering. To evaluate the efficacy of our pretrained model, we conduct rigorous testing on a held-out dataset that represents a diverse range of hazy conditions. Our evaluation metrics include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and a novel perceptual quality metric that we developed specifically for assessing dehazing performance. The results demonstrate the model's strong generalization capabilities, consistently producing clear, natural-looking images across various scenes and haze densities.