Lung functions have significant clinical value for diagnosis of pulmonary diseases. Fourier Decomposition is a non-contrast-enhanced method for assessing pulmonary functions from time-resolved images. However, its performance depends on temporal resolution. Here we propose two compressed sensing reconstruction strategies based on low-rank and sparse matrix decomposition. Retrospective demonstrations on in vivo acquisitions demonstrate the performance of these techniques, enabling improved scan efficiency without degrading image quality.