To address the problems of excessive loss of detail information, unclear texture, and low contrast during the fusion of infrared and visible images, this study proposes an infrared and visible image fusion method based on image enhancement and secondary nonsubsampled contourlet transform (NSCT) decomposition. First, an image enhancement algorithm based on guided filtering is used to improve the visibility of visible images. Second, the enhanced visible and infrared images are decomposed by NSCT to obtain low- and high-frequency subbands, and different fusion rules are used in different subbands to obtain the NSCT coefficient of the first fusion image. The NSCT coefficients of the primary fused image are reconstructed and decomposed into low- and high-frequency subbands, which are then fused with the low- and high-frequency subbands of the visible light image, respectively to obtain the NSCT coefficients of the secondary fused image. Finally, the NSCT coefficients of the secondary fused image are reconstructed by inverse transformation to obtain the final fused image. Numerous experiments are conducted with public datasets, using eight evaluation indicators to compare the proposed method with eight fusion methods based on multiple scales. Results show that the proposed method can retain more details of the source image, improve the edge contour definition and overall contrast of the fusion results, and has advantages in terms of subjective vision and the use of evaluation indicators.