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Reconstruction of block-sparse signals by using an l2/p-regularized least-squares algorithm

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3 Author(s)
Pant, J.K. ; Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, Canada ; Wu-Sheng Lu ; Antoniou, A.

A new algorithm for the reconstruction of so called block-sparse signals in a compressive sensing framework is presented. The algorithm is based on minimizing an ℓ2/p-norm regularized l2 error. The minimization is carried out by using a sequential conjugate-gradient algorithm where the line search involved is carried out using a technique based on Banach's fixed-point theorem. Simulation results are presented which show that for large-size data the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to several known algorithms.

Published in:

Circuits and Systems (ISCAS), 2012 IEEE International Symposium on

Date of Conference:

20-23 May 2012