Abstract: Remote sensing image fusion technology can combine and enhance information from two or more multi-source remote sensing images, making the fused image more accurate and comprehensive. The nonsubsampled contourlet transform (NSCT) is effective in extracting details from high-resolution remote sensing images through multi-scale and multi-directional decomposition, thus achieving image sharpening with high spatial resolution. However, traditional NSCT produces limited high-frequency details and is prone to introduce artifacts such as “ghosting” in fused images. To address this issue, the study proposed a new panchromatic sharpening fusion algorithm for remote sensing images by combining NSCT with a guided filter (GF). Specifically, the promoted algorithm extracted the detail components from histogram-matched images using the multi-scale, multi-direction decomposition and reconstruction properties of the NSCT. Meanwhile, it extracted multi-spectral detail components with panchromatic detail features using GF. Finally, the fused images with high-spatial and high-spectral resolutions were obtained by sharpening based on weighted detail components. The proposed algorithm was proved effective through both subjective and objective evaluations using multiple high-resolution remote sensing datasets.