# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 1 Introduction
Lightweight CNN (Convolutional Neural Network) methods for haze removal have gained considerable attention in recent years [22; 23]. These approaches learn to effectively remove haze by training on large datasets containing pairs of hazy and clean images. The primary advantage of CNN-based methods lies in their ability to learn complex nonlinear transformations, enabling robust performance across various haze conditions and scene types [24; 25]. Models such as AOD-Net and MSCNN have demonstrated excellent haze removal capabilities while maintaining a compact architecture with a relatively small number of layers and parameters [26; 27]. The success of these models has not only improved dehazing performance but also significantly reduced the computational resources required, making real-time dehazing more feasible. To further enhance the performance of deep learning-based haze removal models, researchers have employed fine-tuning techniques [28; 29]. Fine-tuning involves adjusting a pre-trained model to specific datasets or tasks, allowing for significant performance improvements even with limited data. This approach is particularly beneficial for improving haze removal performance in real-world environments, as it enables the model to adapt to the specific haze characteristics of particular regions or times of day [30; 31]. The ability to fine-tune models has greatly increased their versatility, allowing for customized solutions that can be tailored to specific environmental conditions or application requirements. The increasing demand for real-time processing on mobile and edge devices has spurred research into low-power, real-time haze removal technologies [31; 32]. Various lightweight techniques, including network pruning, knowledge distillation, and quantization, are being actively explored and applied in this context. Network pruning reduces model size by eliminating parameters deemed less important, while knowledge distillation transfers knowledge from a larger teacher model to a more compact student model. Quantization techniques reduce memory usage and computational load by representing model parameters with lower bit precision [33]. These approaches collectively aim to significantly reduce model size and computational complexity while minimizing performance degradation. The development of these lightweight models has been crucial in bringing advanced dehazing capabilities to resource-constrained devices, expanding the potential applications of this technology. An emerging area of focus in haze removal research is the development of techniques specifically tailored for static scenes captured by fixed cameras [8; 21]. These cameras are widely used in applications such as traffic monitoring, security surveillance, and environmental monitoring, where effective haze removal can substantially enhance system reliability and efficiency. The static nature of the scenes captured by these cameras presents unique opportunities for optimization. By leveraging prior information about the static elements in the scene, it becomes possible to develop more accurate and efficient haze removal algorithms. For instance, background modeling techniques can be employed to estimate scene information in a haze-free state, enabling more effective haze removal from current images [9; 19]. This specialized approach to dehazing in static scenes has the potential to significantly improve the performance and efficiency of many critical monitoring systems, particularly in areas prone to frequent haze or fog. Despite these advancements, there remains a significant gap in deploying high-quality dehazing algorithms on resource-constrained devices, especially in fixed camera environments commonly found in traffic monitoring and outdoor surveillance systems. Existing methods either lack the efficiency required for real-time processing or do not exploit the static nature of such scenes to optimize performance.