1 research outputs found
Practical approximate projection schemes in greedy signal space methods
Compressive sensing (CS) is a new signal acquisition paradigm which shows
that far fewer samples are required to reconstruct sparse signals than
previously thought. Although most of the literature focuses on signals sparse
in a fixed orthonormal basis, recently the Signal Space CoSaMP (SSCoSaMP)
greedy method was developed for the reconstruction of signals compressible in
arbitrary redundant dictionaries. The algorithm itself needs access to
approximate sparse projection schemes, which have been difficult to obtain and
analyze. This paper investigates the use of several different projection
schemes and catalogs for what types of signals each scheme can successfully be
utilized. In addition, we present novel hybrid projection methods which
outperform all other schemes on a wide variety of signal classes