Uncertainty-Driven Weakly Supervised Dehazing Network: Integrating Dynamic Attention and Multi-Scale Feature Fusion

Haze significantly affects image clarity, hindering vision-based systems like autonomous driving. However, current dehazing methods rely heavily on labeled data and overlook pixel-level uncertainties, limiting real-world effectiveness. To address this, we propose an uncertainty-driven weakly supervised dehazing network with a regional dynamic attention module and a transformer-based multi-scale feature fusion module. Firstly, the regional dynamic attention module employs a channel-pixel collaborative attention mechanism to adjust weights based on haze distribution, enhancing focus on hazy channels and dense regions. Meanwhile, the multi-scale feature fusion module integrates global semantics with local details through window-based multi-head self-attention and parallel dilated convolutions, effectively capturing long-range dependencies while preserving details. Additionally, we introduce an unsupervised loss function based on heteroscedastic uncertainty to guide training by adaptively correcting contrast, enhancing edges, and improving saturation. Extensive experiments on synthetic and real-world images demonstrate that our network shows superior performance in both objective quality and subjective evaluation.