Recovering compressively sampled signals using partial support information

M. P. Friedlander, Hassan Mansour, Rayan Saab, Ozgur Yilmaz

IEEE Trans. on Information Theory, 2011 (to appear)

arxiv

Abstract

In this paper we study recovery conditions of weighted ell_1 minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that if at least 50% of the (partial) support information is accurate, then weighted ell_1 minimization is stable and robust under weaker conditions than the analogous conditions for standard ell_1 minimization. Moreover, weighted ell_1 minimization provides better bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal to be recovered. We illustrate our results with extensive numerical experiments on synthetic data and real audio and video signals.