Ventilation and perfusion functions have significant clinical value for the diagnosis of pulmonary diseases. Fourier Decomposition is a non-contrast-enhanced method for assessing regional ventilation and perfusion information from time-resolved images. However, its robustness suffers from poor temporal resolution. Here we propose a compressed sensing reconstruction of undersampled acquisitions to improve temporal resolution of dynamic images. Retrospective demonstrations on in vivo acquisitions indicate that the proposed reconstruction scheme achieves similar image quality to conventional acquisitions while improving scan efficiency.