Recovering Compressively Sampled Signals Using Partial Support Information

Michael P. Friedlander, Hassan Mansour, Rayan Saab, Özgür Yilmaz

Abstract: We study recovery conditions of weighted l1 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 l1 minimization is stable and robust under weaker sufficient conditions than the analogous conditions for standard l1 minimization. Moreover, weighted l1 minimization provides better upper 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.