To overcome the disadvantages of low contrast and missing target information for the existing synthetic aperture radar (SAR) and visible image fusion methods, an image enhanced fusion algorithm based on texture segmentation and top-hat transformation is proposed. Entropy texture image which is generated by gray level co-occurrence matrix of SAR image is segmented by threshold, and the region of (ROI) interest of SAR is extracted. The SAR and optical images are decomposed by the non-subsampled contourlet transform (NSCT). A region fusion rule is introduced to the low-frequency coefficient, and low-frequency coefficient of SAR is chosen in the region of interest. The significant bright and dark image detail features are extracted by top-hat transformation and the low-frequency synthetic coefficient is obtained through adding the above bright and dark features into low-frequency coefficient. High-frequency subband coefficients are fused by selecting maximum significant factor of local directional entropy. The fused image is obtained by the NSCT inverse transformation of the fused coefficient. The experiment results testify the validity of the proposed image fusion algorithm.