Although higher-mode surface wave dispersion curves can provide additional constraints on the subsurface velocity structure, their extraction from ambient noise data remains more intractable than the extraction of fundamental-mode dispersion curves. Recently, the frequency-Bessel transform (F-J) method was developed to extract multimodal dispersion curves from ambient noise. Here, we propose an alternative compressed sensing (CS) method for extracting multimodes from ambient noise. We solve the CS inverse problem by using two methods: an l1-based optimization algorithm and a Bayesian method. Synthetic and field data examples are conducted to validate our method. The dispersion curves extracted by our method are consistent with those extracted by the F-J method, but our method is more efficient and can extract higher-resolution dispersion energy images than the F-J method. Our method can quickly and reliably extract multimodes from ambient noise, thereby facilitating studies of ambient noise tomography.