To address issues, such as loss of spectral and spatial detail as well as unclear fusion results during the fusion process, a fusion method based on particle swarm optimization is proposed. The initial step of this method involves preprocessing the original image to derive edge detection matrices for each of the image's channels. Subsequently, the spectral coverage coefficient is determined by employing the least square method to generate a more precise image. Finally, an adaptive injection model framework is proposed, which incorporates a weighted matrix, particle swarm optimization, and error relative global accuracy (ERGAS) index function to optimize the weights for edge detection. The band weights in the dataset are calculated to generate the final fused image. In this study, the performance of five fusion methods is assessed using three remote sensing satellite images of varying resolution (WorldView-2, GF-2, and GeoEye) by quantitatively analyzing six evaluation indicators. The results indicate that the method proposed in this paper outperforms other methods in terms of subjective visual effects and objective quantitative evaluation indicators such as average gradient and spatial frequency. Furthermore, the proposed method realizes a good fusion effect in retaining spectral and spatial information.