A dynamic hybrid expert framework with encoder–decoder interaction for robust image enhancement in train environment perception

Abstract

Train environment perception technology is one of the critical factors in ensuring safe train operations, particularly in challenging conditions such as foggy, rainy weather, and poorly lit environments like tunnels. The clarity of images directly influences the accuracy of obstacle detection and decision-making processes during train operation. However, existing image restoration methods are typically tailored to single scenarios, making them inadequate for the diverse and complex environmental variations encountered during train operations. Most of these methods lack specificity, rendering them ineffective in handling complex textures and fine details, resulting in suboptimal image quality under adverse conditions, often plagued by blurriness and noise interference. To address these challenges, we propose a dynamic hybrid expert image restoration framework specifically designed for train environment perception. This framework integrates multiple expert modules and a dynamic weight generation mechanism, enabling flexible adaptation to various environmental characteristics. Specifically, the framework comprises multiple expert modules, each focusing on distinct feature extraction tasks, thereby enhancing image clarity and detail restoration in challenging conditions such as foggy weather, low-light situations, and tunnels. The system dynamically generates weights based on the input image characteristics, allowing for the seamless integration of features extracted by each expert, which significantly improves image clarity and detail restoration. Additionally, the interaction between encoder–decoder attention mechanisms enhances the fusion of global and local information, ensuring robust image restoration in complex environments. Experimental results demonstrate that our method performs exceptionally well across various train operating conditions, particularly in foggy image enhancement and low-light image restoration in tunnels. Compared to existing methods, our approach achieves superior restoration quality and efficiency. Our method significantly enhances the image processing capabilities of train environment perception systems, providing a robust safeguard for safe train operations.