Frequency-Attentive Multiscale Network for Real-Time Adverse Weather Restoration in Railway Monitoring

Rain and fog significantly degrade visibility in rail transit monitoring systems due to their non-uniform spatial distribution and dynamic temporal variations. Traditional image restoration methods struggle to balance computational efficiency and edge preservation under extreme weather conditions. We propose a Frequency-Adaptive Multi-Scale Network (FAMNet) that jointly optimizes wavelet-based spectral decomposition and similarity-driven feature aggregation. Our framework dynamically models degradation patterns through adaptive spectral kernels while capturing cross-region dependencies via similarity-driven aggregation. Evaluated on 8500 synthetic rail transit images derived from the RailSem19 benchmark, the method achieves 32.6 dB PSNR/0.95 SSIM for deraining (+1.0 dB/+4% vs. Restormer). Our method, evaluated on an NVIDIA RTX 3080 GPU with 256 × 256 inputs (batch size 1, FP32 precision), achieves an end-to-end inference speed approximately 40× faster than Restormer—a leading baseline in restoration quality—while maintaining competitive performance. This efficiency shows strong potential for cost-effective deployment in all-weather rail transit inspection systems, with possible applications in safety-critical scenarios such as track obstacle detection.