Many image fusion methods, such as large image features to fuse image coefficients, ignore the correlation of edge features between images, resulting in block phenomenon and discontinuity in the fused image. This paper designs an image fusion algorithm with edge feature weighting based on the second generation of curved wave transform. Firstly, the input image is decomposed into low frequency and high frequency coefficients by Second Generation Curvelet(SGC) transform. Then, the region energy model is employed to calculate the energy features of the image, and the mean value model is adopted to calculate the brightness features of the image. By using the energy and brightness features of the image, the fusion function of low-frequency coefficients with two features is constructed to obtain the fusion low-frequency coefficients with excellent energy and brightness features. Sobel operator is introduced to detect the edge features of the image, and the fusion function of high frequency coefficients weighted by the edge features is constructed based on the detection results to obtain the fusion results. Finally, the fusion experiments are carried out with the algorithm. The experimental results show that the proposed algorithm has better fusion effect than current algorithms, and the edge continuity and clarity of the fusion image are better.