To address the issue of spatial and spectral information fusion during pansharpening, this study proposed a sharpening model for panchromatic and multispectral images based on the multiscale weighted pulse-coupled neural network (PCNN) and low-rank and sparse decomposition in the non-subsampled shearlet transform (NSST) domain. The sharpening model consists of low- and high-frequency processing modules. For high-frequency subbands, a method for weighting high-frequency subbands in various scales and directions was proposed, accompanied by an adaptive PCNN model tailored to their characteristics in different directions. In contrast, low-frequency subbands were decomposed into low-rank and sparse parts, with corresponding fusion rules created according to their characteristics. The fused image was then obtained through inverse NSST. The experiments on the sharpening model were conducted using GeoEye,QuickBird, and Pléiades datasets. Moreover, an ablation experiment was designed for the multiscale weighting module for high-frequency information. Compared to suboptimal models, the sharpening model in this study increased the peak signal-to-noise ratio (PSNR) value by approximately 1 dB, 1.6 dB, and 2.2 dB, respectively. The experimental results demonstrate that the sharpening model outperformed other algorithms in index assessment, effectively resolving the challenge of extracting high-frequency information.