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Research Article Open access CC BY 3.0

Sufficient Conditions for CS-recovery

Hiroshi Inoue

Journal of Advances in Mathematics and Computer Science · pp. 184–198 · Published 23 Oct 2013

10.9734/BJMCS/2014/6171

Abstract

In this paper we define the k-restrictly norm constant rk(A) of a matrix A to be used in compressed sensing and give better error estimations on recovering compressive signals with noise using the matrix A~ _ A rk(A) . Furthermore, we define the notion of k-restricted invertibility of A, which is equivalent to that A~ _ A=rk(A) obeys the RIP of order k. And by using the Q. Mo and S. Li idea and T. Cai and A. Zhang idea, we establish the sufficient condition for the restricted isometry constant _~k (k _ s) of A~ under the assumption that A is k-restrictly invertible. In particular, if ~_s < 0:5 and ~_2s < 0:828, then an unknown compressive signal with noise can be recovered.

Compressed sensing Restricted norm constants Restricted invertible Restricted isometry constants Restricted isometry property Sparse approximation Sparse signal recovery.

Cited by 1

WITHDRAWN: RIPless theory for compressed sensing

Hiroshi Inoue · Applied and Computational Harmonic Analysis · 2014

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