The quest for optimal sensing matrices is crucial in the design of efficient Compressed Sensing architectures. In this paper we propose a maximum entropy criterion for the design of optimal Hadamard sensing matrices (and similar deterministic ensembles) when the signal being acquired is sparse and non-white. Since the resulting design strategy entails a combinatorial step, we devise a fast evolutionary algorithm to find sensing matrices that yield high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals. © 2014 IEEE.
Maximum entropy hadamard sensing of sparse and localized signals / Cambareri, Valerio; Rovatti, Riccardo; Setti, Gianluca. - STAMPA. - (2014), pp. 6854021.2357-6854021.2361. (Intervento presentato al convegno 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 tenutosi a Florence, ita nel 2014) [10.1109/ICASSP.2014.6854021].
Maximum entropy hadamard sensing of sparse and localized signals
CAMBARERI, VALERIO;ROVATTI, RICCARDO;
2014
Abstract
The quest for optimal sensing matrices is crucial in the design of efficient Compressed Sensing architectures. In this paper we propose a maximum entropy criterion for the design of optimal Hadamard sensing matrices (and similar deterministic ensembles) when the signal being acquired is sparse and non-white. Since the resulting design strategy entails a combinatorial step, we devise a fast evolutionary algorithm to find sensing matrices that yield high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals. © 2014 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
