Magnetic resonance imaging (MRI) can generate images with varying contrasts and acquisition times depending on imaging parameters. Utilizing a high-resolution contrast with a short acquisition time as a reference for the super-resolution (SR) of low-resolution contrasts with long acquisition times is effective for the rapid acquisition of high-quality images. However, existing methods mainly process features in the spatial domain, and overlook potential features in the frequency domain. This paper proposes Multiscale Spatial-Frequency Collaborative Fusion Network (MSCFN), which jointly leverages information in the spatial and frequency domains for SR. A global-local fusion block optimizes global structural features and local texture details at different scales, and an adaptive low-high frequency fusion module utilizes the complementary nature of multiple contrasts to decompose reference images into high- and low-frequency components and adaptively fuse them to enhance feature integration. Experimental results indicate that MSCFN outperforms existing multicontrast MRI SR methods. The code is publicly available at https://github.com/crystal177/MSCFN.