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Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples

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3 Author(s)
Zonoobi, D. ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore ; Kassim, A.A. ; Venkatesh, Y.V.

Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the l0 norm, even though, in practice, the l1 or the lp ( 0 <; p <; 1) (pseudo-) norm is preferred. In this paper, we explore the use of the Gini index (GI), of a discrete signal, as a more effective measure of its sparsity for a significantly improved performance in its reconstruction from compressive samples. We also successfully incorporate the GI into a stochastic optimization algorithm for signal reconstruction from compressive samples and illustrate our approach with both synthetic and real signals/images.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 5 )