In order to overcome the shortcomings of many current image fusion algorithm, such as discontinuity and ringing, which are mainly achieved by taking large image coefficients and ignoring the correlation between images, a multifocus image fusion algorithm based on nonsubsampled shearlet transform and guidance rule is designed in this paper. Firstly, the nondown sampling Shearlet transform (NSST) is introduced to calculate the multifocus image and obtain the different coefficients of the image. Secondly, the image correlation is measured by using the regional energy, standard deviation and spatial frequency characteristics of the image, and the measurement results are used as guidance information for selecting fusion rules, and the lowfrequency coefficient fusion is completed by constructing guidance rules. When high frequency coefficients are fused, the brightness and edge information of the image are measured by means of the mean value feature of the image and the Laplacian energy feature, respectively, in order to achieve the fusion of high frequency coefficients. The experimental results show that, compared with the current fusion algorithm, the fusion image quality of this algorithm is better and has better fusion performance.