Maximum Entropy Hadamard Sensing of Sparse and Localized Signals

Authors:
Valerio Cambareri, Riccardo Rovatti, Gianluca Setti

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 yielding high-entropy measurements. Experimental results exploiting this strategy show quality gains when performing the recovery of optimally sensed small images and electrocardiographic signals.


Publication Status:
Accepted, 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing.
Available on IEEEXplore.

BibTeX Entry:
@inproceedings{cambareri2014maximum,
  title={Maximum entropy hadamard sensing of sparse and localized signals},
  author={Cambareri, Valerio and Rovatti, Riccardo and Setti, Gianluca},
  booktitle={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={2357--2361},
  year={2014},
  organization={IEEE}
}
Attachments:
Supporting code on Google Drive.
Requires WaveLab 8.50 and SPGL1 v1.7 (or optionally Gurobi).

Maximum Entropy Hadamard Sensing


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