Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in addition to high spatial resolution. Due to the nature of data acquisition in current MRI systems, there exists a trade-off between temporal and spatial resolution. In this work, we present two methods for improving the spa-tiotemporal resolution in dynamic MRI using compressive sampling (CS). Experimental results illustrate that the proposed Bayes least squares-Gaussian scale mixtures (BLS-GSM) model-based CS algorithm compares favorably with other state-of-the-art compressive dynamic MRI techniques.