MODIFIED BASIS PURSUIT DENOISING(MODIFIED-BPDN) FOR NOISY COMPRESSIVE SENSING WITH PARTIALLY KNOWN SUPPORT

In this work, we study the problem of reconstructing a sparse signal from a limited number of linear 'incoherent' noisy measurements, when a part of its support is known. The known part of the support may be available from prior knowledge or from the previous time instant (in applications requiring recursive reconstruction of a time sequence of sparse signals, e. g. dynamic MRI). We study a modi cation of Basis Pursuit Denoising (BPDN) and bound its reconstruction error. A key feature of our work is that the bounds that we obtain are computable. Hence, we are able to use Monte Carlo to study their average behavior as the size of the unknown support increases. We also demonstrate that when the unknown support size is small, modi ed-BPDN bounds are much tighter than those for BPDN, and hold under much weaker sufficient conditions (require fewer measurements).