Multi-Channel Attention Fusion Algorithm for Railway Image Dehazing

Abstract

Railway safety inspections, a critical component of modern transportation systems, face significant challenges from adverse weather conditions, like fog and rain, which degrade image quality and compromise inspection accuracy. To address this limitation, we propose a novel deep learning-based image dehazing algorithm optimized for outdoor railway environments. Our method integrates adaptive high-pass filtering and bilateral grid processing during the feature extraction phase to enhance detail preservation while maintaining computational efficiency. The framework uniquely combines RGB color channels with atmospheric brightness channels to disentangle environmental interference from critical structural information, ensuring balanced restoration across all spectral components. A dual-attention mechanism (channel and spatial attention modules) is incorporated during feature fusion to dynamically prioritize haze-relevant regions and suppress weather-induced artifacts. Comprehensive evaluations demonstrate the algorithm’s superior performance: On the SOTS-Outdoor benchmark, it achieves state-of-the-art PSNR (35.27) and SSIM (0.9869) scores. When tested on a specialized railway inspection dataset containing 12,840 fog-affected track images, the method attains a PSNR of 30.41 and SSIM of 0.9511, with the SSIM being marginally lower (0.0017) than DeHamer while outperforming other comparative methods in perceptual clarity. Quantitative and qualitative analyses confirm that our approach effectively restores critical infrastructure details obscured by atmospheric particles, improving defect detection accuracy by 18.6 percent compared to non-processed images in simulated inspection scenarios. This work establishes a robust solution for weather-resilient railway monitoring systems, demonstrating practical value for automated transportation safety applications.