Research on image fusion algorithm based on K-means clustering and NSCT

Traditional NSCT-based image fusion algorithms usually first perform NSCT transformation on the original image, and then perform the fusion of different scale coefficients, without specific analysis of the original image. In this regard, an image fusion algorithm based on K-means clustering is proposed. First use the K-means algorithm clustering to classify images, and then perform NSCT decomposition on the segmented images to obtain low-frequency and high-frequency subband coefficients. According to the characteristics of the segmented image, an adaptive weighted fusion method is used to fuse the coefficients. Finally, the fusion coefficients are inversely transformed by NSCT to obtain the final fusion image. Experimental results show that the algorithm is more effective in preserving image texture information and improving contrast, and the objective quality evaluation results are also better.