Existing infrared and visible image fusion methods cannot effectively balance the unique and similar structures of infrared and visible images, resulting in suboptimal visual quality. To address these problems, this study proposes a cross-fusion Transformer-based fusion method. The cross-fusion Transformer block is the core of the proposed network, which applies a cross-fusion query vector to extract and fuse the complementary salient features of infrared and visible images. This cross-fusion query vector balances the global visual characteristics of infrared and visible images and effectively improves the fusion visual effect. In addition, a multi-scale feature fusion block is proposed to address the problem of information loss caused by the down-sampling operation. Experimental results on the TNO, INO, RoadScene, and MSRS public datasets show that the performance of the proposed method surpasses existing representational deep-learning-based methods. Specifically, comparing with the suboptimum results on the TNO dataset, the proposed method obtains ~27.2%, ~29.2%, and ~9.9% improvements in terms of standardized mutual information, mutual information, and visual fidelity metrics, respectively.