Single-image dehazing is still a challenging problem because haze mainly corrupts low-frequency structures such as global contrast and color consistency, while fine textures and object boundaries are degraded in a different manner. In this paper, we present a frequency-guided multi-scale dehazing network (FGDNet) that explicitly couples spatial-domain restoration and Fourier-domain feature decomposition in a compact U-Net-like architecture. Built on a gated U-Net backbone, the proposed model inserts a frequency processing branch into encoder stages. In detail, the feature maps are transformed by fast Fourier transform, split into low- and high-frequency components through a radial mask, refined separately, and fused by a lightweight cross-domain gating module. The low-frequency pathway emphasizes color and illumination recovery, whereas the high-frequency pathway enhances edges and textures. Moreover, an additional Fourier amplitude supervision term aligns the spectral distribution of restored images with haze-free targets. Experimental results on RESIDE ITS, RESIDE OTS, O-HAZE, and NH-HAZE show that the proposed method achieves 33.3 dB PSNR/0.983 SSIM on ITS, 35.1 dB PSNR/0.988 SSIM on OTS, 19.1 dB PSNR/0.786 SSIM for OTS-trained generalization to O-HAZE, and 15.8 dB PSNR/0.648 SSIM for OTS-trained generalization to NH-HAZE. Furthermore, both quantitative and qualitative results demonstrate that the proposed method provides a more effective and more robust solution than representative dehazing methods. In addition, ablation studies confirm that both the Fourier branch and the spatial–spectral gating mechanism contribute consistently to performance gains. These results support the effectiveness of explicit frequency-aware representation learning for image dehazing and suggest a practical direction for improving generalization from synthetic to real haze.
Jin et al. (2026) studied this question.