At present, many image fusion algorithms mainly use image energy information to fuse image coefficients, ignoring the texture information of the image, which brings the defects of Gibbs and block phenomenon to the fusion results. This paper designs a multi-focus image fusion algorithm based on the second generation Curvelet transform coupled with texture information adjustment. Firstly, the second generation Curvelet transform is utilized to obtain different sub-band images from the input image. Then, the texture information factor is constructed by using R,G and B values of the image, and the texture information of the image is measured. By combining the information entropy characteristics of the image and the R, G and B values of the image, the fusion results have more texture information. The average gradient feature of the image is adopted to compute high frequency coefficient fusion, which makes it more capable of describing details such as edges. Finally, the image fusion test of this algorithm shows that compared with current fusion algorithms, the fusion image of this proposed algorithm is clearer, without the defects of Gibbs and block phenomenon, and with larger values of mutual information and standard deviation.