# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 1 Introduction
Our research addresses this gap by developing an progressive pruning method tailored for Light Dehaze Networks in static scenes, enabling efficient deployment without sacrificing dehazing quality. In light of these developments, this study aims to advance the field of image dehazing by focusing on lightweight haze removal networks using importance-based channel pruning techniques [31; 32]. Our approach involves evaluating the importance of each channel in the network and removing less critical channels to reduce the model size. This method effectively balances model compression with performance preservation. We apply this technique to develop a lightweight model specifically optimized for fixed camera environments. By fine-tuning this lightweight model for specific scenes, we aim to achieve high-quality haze removal performance with real-time processing capabilities, potentially significantly improving the efficiency of fixed camera systems even with limited computing resources. In this paper, we aim to develop an progressive pruning method for Light Dehaze Networks specifically optimized for fixed camera environments. Our main contributions are as follows:  
* Fine-Tuning Strategy for Static Scenes: We propose a scene-specific fine-tuning approach that enhances dehazing performance by adapting the model to the unique characteristics of a fixed camera scene. * Channel Importance Analysis: We introduce an importance-based channel analysis method to evaluate the contribution of each network channel to dehazing performance, guiding the pruning process. * Progressive Pruning Algorithm: We develop a progressive pruning algorithm that considers layer-wise sensitivity, effectively reducing model complexity while maintaining dehazing quality within a specified threshold. By integrating these strategies, we achieve a lightweight dehazing model suitable for deployment in resource-constrained environments, such as traffic monitoring and outdoor surveillance systems. The structure of this paper is as follows. Section 2 reviews the related work on image dehazing techniques, including traditional methods, deep learning approaches, model pruning techniques, and dehazing for static scenes captured by fixed camera environments. Section 3 details our proposed methods, describing the network architecture for dehazing, implementation details, pretraining and fine-tuning strategies, localized fine-tuning for static viewpoint imagery, channel importance analysis, the fine-tuned pruned network, and our progressive pruning algorithm. Section 4 presents the experimental results and performance analysis of the lightweight network, verifying the effectiveness of the pro posed algorithm. Through experiments under various haze conditions and scenes, we evaluate the robustness and generalization ability of our method. Additionally, we analyze the computational complexity, memory usage, and processing speed compared to existing methods. Section 5 interprets our findings, discussing the limitations of the current study and potential improvements. Finally, Section 6 summarizes the study and suggests future research directions, reflecting on the future prospects of haze removal technology in advancing computer vision applications and the growing demand for efficient, real-time image processing solutions.