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|Title:||Gini index as sparsity measure for signal reconstruction from compressive samples||Authors:||Zonoobi, D.
|Keywords:||Compressive sensing (CS)
Gini index (GI)
simultaneous perturbation stochastic approximation (SPSA)
|Issue Date:||Sep-2011||Citation:||Zonoobi, D., Kassim, A.A., Venkatesh, Y.V. (2011-09). Gini index as sparsity measure for signal reconstruction from compressive samples. IEEE Journal on Selected Topics in Signal Processing 5 (5) : 927-932. ScholarBank@NUS Repository. https://doi.org/10.1109/JSTSP.2011.2160711||Abstract:||Sparsity is a fundamental concept in compressive sampling of signals/images, which is commonly measured using the 0 norm, even though, in practice, the 1 or the p (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. © 2011 IEEE.||Source Title:||IEEE Journal on Selected Topics in Signal Processing||URI:||http://scholarbank.nus.edu.sg/handle/10635/82420||ISSN:||19324553||DOI:||10.1109/JSTSP.2011.2160711|
|Appears in Collections:||Staff Publications|
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