A fusion algorithm based on target extraction for infrared image (IIR) and visible image fusion in the nonsubsampled contourlet transform (NSCT) domain is proposed. Commonly, the target information in IIR is important; in order to fully retain the target information in a final fused image, first, use maximum between-class variance method to segment IIR, such that the target regions with salient objects are extracted to produce the background and target images. Next, the visible and background images are decomposed to a series of low-pass and band-pass images by NSCT, respectively. Then, fuse the obtained images to produce the fused background image by different strategies in each band, in which Gaussian fuzzy logic is used to produce the low-pass coefficient; the spatial frequency of each band-pass image is used to determine the linking strength β value of pulse coupled neural network structure, and the result is used to fuse the band-pass images. Eventually, the fused image is produced combining the target image and the fused background image. The experiments show that this algorithm can retain more background details of the two images and highlight the target in the infrared image more effectively, as well as obviously improve the visual effect of the fusion image.