This paper designs and evaluates a variant of CoSaMP algorithm, for recovering\r\nthe sparse signal s from the compressive measurement v (Uw s) given a fixed lowrank\r\nsubspace spanned by U. Instead of firstly recovering the full vector\r\nthen separating the sparse part from the structured dense part, the proposed algorithm\r\ndirectly works on the compressive measurement to do the separation. We investigate the\r\nperformance of the algorithm on both simulated data and video compressive sensing. The\r\nresults show that for a fixed low-rank subspace and truly sparse signal the proposed\r\nalgorithm could successfully recover the signal only from a few compressive sensing (CS)\r\nmeasurements, and it performs better than ordinary CoSaMP when the sparse signal is\r\ncorrupted by additional Gaussian noise.
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