Coupling Frequency-domain Stepwise Enhancement and Statistical Moment Adjustment for Single Image Dehazing

Single Image Dehazing is a challenging ill-posed problem. While Transformers excel in long-range modeling, the low-pass characteristic of their self-attention further obscures high-frequency scene details attenuated by scattering. Furthermore, haze-degraded encoder features exhibit Mean-Elevation and Variance-Compression, leading to a significant statistical discrepancy with clear decoder features that hinders effective feature fusion. To address these limitations, this paper proposes the Frequency-Domain Stepwise and Moment Adjustment Network (FDSMA-Net). First, we design the High-Frequency Disentangled Attention (HFDA) mechanism, which explicitly decouples high-frequency components via attention inversion and utilizes a Dual Dynamic Frequency Modulation mechanism to calibrate and enhance details, effectively overcoming the spectral bias of standard attention. Second, the Moment-Driven Channel Fusion Attention (MDCFA) mechanism is introduced in the skip connections of the U-Net architecture. By explicitly aligning the first-order moment and second-order moment, MDCFA dynamically corrects the distribution shift caused by haze, ensuring statistically aligned feature fusion. Enabled by frequency disentanglement and moment alignment, FDSMA-Net excels on synthetic and real benchmarks, effectively recovering scene details and enhancing visual clarity. The source code of our proposed FDSMA-Net will be released at https://github.com/SWU-CS-MediaLab/FDSMA-Net.