SegOMP: Sparse Recovery with Fewer Measurements

Li ZENG
Xiongwei ZHANG
Liang CHEN
Weiwei YANG

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E97-A    No.3    pp.862-864
Publication Date: 2014/03/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E97.A.862
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Digital Signal Processing
Keyword: 
compressed sensing,  sparse recovery,  segment OMP,  measurements,  

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Summary: 
Presented is a new measuring and reconstruction framework of Compressed Sensing (CS), aiming at reducing the measurements required to ensure faithful reconstruction. A sparse vector is segmented into sparser vectors. These new ones are then randomly sensed. For recovery, we reconstruct these vectors individually and assemble them to obtain the original signal. We show that the proposed scheme, referred to as SegOMP, yields higher probability of exact recovery in theory. It is finished with much smaller number of measurements to achieve a same reconstruction quality when compared to the canonical greedy algorithms. Extensive experiments verify the validity of the SegOMP and demonstrate its potentials.