Fog Removal Study on Fog Point Distribution and Multi-Scale Dynamic Thresholding in the Field of Point Cloud Defogging

Ensuring reliable perception under adverse weather is critical for Cyber-Physical Systems (CPS) in intelligent railway applications. LiDAR, as a key sensor, provides high-precision 3D data but suffers from severe degradation in fog due to signal scattering and attenuation, introducing noise into point clouds and disrupting CPS feedback loops. This paper proposes a CPS-oriented point cloud defogging framework that estimates fog distribution based on attenuation modeling and local density analysis. A multi-scale adaptive thresholding method is introduced, dynamically adjusting denoising parameters by combining a base multiscale factor with density-driven adaptive scaling. Dynamic neighborhood statistics and height-based corrections further refine the noise removal process, enhancing the distinction between fog noise and true object structures. Experimental evaluations on synthetic and real-world foggy datasets show that the proposed method outperforms traditional techniques such as SOR, DSOR, DROR in terms of precision, recall and structural preservation. By improving the reliability of the perception layer, our approach reinforces the overall robustness and safety of CPS-driven railway systems operating in foggy environments, providing a lightweight and adaptable solution suitable for real-time deployment.