Maximum Entropy Hadamard Sensing of Sparse and Localized Signals

Valerio Cambareri, Riccardo Rovatti, Gianluca Setti

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:
  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)},
Supporting code on Google Drive.
Requires WaveLab 8.50 and SPGL1 v1.7 (or optionally Gurobi).

Maximum Entropy Hadamard Sensing