Wavelet-Attention Transformer-Based Traffic Image Dehazing for Intelligent and Connected Transportation Systems

In intelligent and connected transportation systems, surveillance cameras serve as critical perception devices for real-time monitoring of traffic and lane conditions. However, haze causes light scattering and contrast reduction, degrading high-frequency details (edges, textures) and semantic information (traffic participants) in camera images. Existing methods face two major challenges in handling hazy images: (1) traditional CNNs struggle to recover high-frequency details and structures lost in haze; (2) existing methods lack explicit modeling of spatially non-uniform haze. To resolve these problems, this paper uses cascaded wavelet transform convolution to reconstruct high-frequency details. Subsequently, this paper designs a dual residual attention mechanism that emphasizes crucial semantic regions and high-frequency details in both channel and spatial dimensions. Lastly, global dehazing is achieved by Swin Transformer–based image modeling, where hierarchical window attention and residual connections effectively capture multi-scale features and long-range dependencies in traffic scenes. Experimental results show that the proposed non-uniform haze removal model improves the robustness of IoT-enabled traffic monitoring. It achieves average improvements of at least 10.45%, 4.80%, and 10.77% in PSNR, SSIM, and VSNR, respectively, and reduces LPIPS by no less than 20.44%.