Unsupervised Dehazing of Real-World Images with Frequency-Aware Learning

Image dehazing remains a challenging task in real-world scenarios. Unlike synthetic datasets, real-world hazy images often exhibit nonuniform atmospheric degradation, severe haze accumulation, and significant texture loss, making it difficult for existing methods to restore realistic appearances and fine details consistently. To address these challenges, we propose a frequency-aware learning-based unpaired image dehazing network for real-world hazy scenes. First, we propose a content-aware state space modelling paradigm. Focus on details through high-frequency enhancement and incorporate global contextual understanding at the encoding stage, enabling adaptive representation of complex textures. Second, to effectively handle spatially nonuniform degradation, a hazy density estimation module is designed to guide multiple expert-gated feedback units, which dynamically select feature fusion paths. Finally, we propose a contour-guided differentiable frequency domain enhancement mechanism to explicitly recover edge and texture details in degraded regions. Extensive experiments on real-world hazy datasets under unsupervised settings demonstrate that our method achieves competitive performance, validating its effectiveness and strong practical potential under complex atmospheric conditions. The code is available at https://github.com/Fan-pixel/FAL-Net.