Existing infrared and visible image fusion methods lack resistance to adverse weather degradation and interference in complex rainy environments. In addition, these methods rarely integrate frequency-domain information effectively during feature extraction, failing to effectively separate degenerate components that exhibit significant differences in their frequency domain characteristics. To address these limitations, this paper proposes a frequency-domain enhanced fusion method (FEFusion) for infrared and visible images. First, this paper introduces a Histogram Transformer (Histoformer), which employs a binning-based self-attention mechanism to dynamically group pixel intensities into different ranges. This enables adaptive focusing on degraded regions and facilitates cross-modal feature association. Second, we design a Frequency Domain Enhancement Block (FDEB) to decompose high-frequency rain-streak noise and low-frequency fog-like effects in the frequency domain. Subsequently, a Low-light Enhancement Module (LEM) is applied to enhance details in dark regions, while a High-Low Frequency Interaction Module (H-LFIM) achieves cross-frequency feature modulation. Finally, the experimental results verify the effectiveness of the method proposed in this work.