Denoising diffusion fusion network for semantic segmentation based on degradation analysis modeling with graph networks

Accurate semantic segmentation is critical for autonomous driving safety, yet real-world degradations severely compromise segmentation robustness, risking safety-critical failures. Current research primarily focuses on architectural innovations and optimization strategies to improve fusion quality. However, these approaches suffer from poor interpretability and inadequate adaptability in adverse weather conditions such as extreme illumination and noise. To address these issues, we propose a denoising diffusion fusion network for semantic segmentation based on degradation modeling. We first construct a multi-scale wavelet-domain fusion network to address the distribution characteristics of different degradations. Building on this decomposition, the denoising diffusion process and wavelet-domain fusion operations are combined to enhance fusion quality. Finally, we develop a denoising diffusion-optimized fusion loss function to guide parameter optimization while suppressing degradation artifacts. Extensive experiments on public datasets show that the proposed method achieves state-of-the-art performance with measurable gains: 42.8% improvement in edge integrity, 37.6% higher spatial frequency for texture preservation, and 39.8% reduction in noise artifacts. Through graph network analysis, we also reveal the interplay mechanisms among different degradations, various fusion quality assessment metrics, and semantic segmentation performance. These advancements exhibit superior robustness to degradations and enhance safety for real-world autonomous systems.