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.
Original language | English (US) |
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Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2357-2361 |
Number of pages | 5 |
ISBN (Print) | 9781479928927 |
DOIs | |
State | Published - Jan 1 2014 |
Externally published | Yes |