Abstract: To address the issues of feature information loss, inaccurate threshold settings, and detail information loss due to low resolution during the fusion process of traditional Dynamic Threshold Neural P (DTNP) systems, a multi-modal medical image super-resolution fusion method based on a feature link model optimization, called Feature Link DTNP (FL-DTNP) system, is proposed. This method uses adaptive link strength for image feature extraction. First, bilinear interpolation and Non-Subsampled Shearlet Transform (NSST) are applied to enhance and decompose the image. Then, the FL-DTNP system is used to fuse the detail and edge information of the high-frequency sub-bands. For the low-frequency sub-bands, a visually significant weighted local energy approach—based on an eight-neighborhood Laplacian weighting—is applied to merge the low-frequency sub-bands. Finally, inverse NSST is used to reconstruct the high-frequency and low-frequency sub-bands. Experiments were conducted on the Harvard dataset, comparing the proposed method with eight classic fusion methods based on multi-scale transformations, deep learning, and sparse representation across seven evaluation metrics. The results demonstrate that the proposed method exhibits outstanding performance and is capable of generating high quality fused images.