Please use this identifier to cite or link to this item:
https://doi.org/10.1109/JSTSP.2011.2160711
Title: | Gini index as sparsity measure for signal reconstruction from compressive samples |
Authors: | Zonoobi, D. Kassim, A.A. Venkatesh, Y.V. |
Keywords: | Compressive sensing (CS) Gini index (GI) non-convex optimization simultaneous perturbation stochastic approximation (SPSA) sparsity measures |
Issue Date: | Sep-2011 |
Source: | 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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