Traditional image fusion algorithm has limitations, such as indistinct target, unclear or missing edge and texture details, and reduced contrast. An infrared and visible image fusion algorithm based on a guided filter (GF) and dual-tree complex wavelet transform (DTCWT) is proposed. First, GF enhancement is performed on visible and high-frequency infrared image components before and after DTCWT decomposition, respectively, according to the characteristics of infrared and visible images. Then, according to the characteristics of different frequency band coefficients, an algorithm based on saliency adaptive weighting rules is proposed to fuse infrared and visible low-frequency subband components; further, a rule based on Laplace energy sum (SML) and gradient value vector is used to fuse high-frequency subbands at different scales and directions. Finally, the fused high- and low-frequency coefficients are inverted using DTCWT to obtain the final reconstructed image. The proposed algorithm is compared with six efficient fusion algorithms. The experimental results demonstrate the improved performance of the proposed algorithm across four objective evaluation indicators with significant target features in different scenes, clear background texture and edge details, and appropriate overall contrast.