As typical complementary modalities, infrared and visible images integrate spectral information to enhance perceptual quality and scene understanding, particularly under challenging imaging conditions. However, resolution disparity between modalities, which is caused by sensor limitations and hardware heterogeneity, poses significant challenges for effective fusion. To address the issue mentioned above, we propose a Mixture-of-Experts (MoE)-based fusion framework specifically designed for heterogeneous-resolution infrared and visible image pairs, named HeRIF. Specifically, the dual-branch encoder-decoder from HeRIF reconstructs low-resolution semantics and high-resolution residuals, with the fusion model exploiting cross-modal high-resolution guidance to refine low-resolution outputs and improve visual fidelity. The restoration module incorporates two MoE units, each dedicated to processing the output features of a distinct encoder, thereby facilitating high-resolution detail restoration tailored to inputs of varying resolutions. Extensive experiments demonstrate that our method outperforms state-of-the-art baselines in terms of fusion quality and downstream tasks, providing a scalable and robust solution for multi-resolution image fusion.