  Motivated by the well-known Papoulis-Gerchberg algorithm, an iterative
thresholding algorithm for recovery of sparse signals from few observations is
proposed. The sequence of iterates turns out to be similar to that of the
thresholded Landweber iterations, although not the same. The performance of the
proposed algorithm is experimentally evaluated and compared to other
state-of-the-art methods.
