Sparsity estimation from compressive projections via sparse random matrices

@article{Ravazzi2018SparsityEF,
  title={Sparsity estimation from compressive projections via sparse random matrices},
  author={Chiara Ravazzi and Sophie Marie Fosson and Tiziano Bianchi and Enrico Magli},
  journal={Eurasip Journal on Advances in Signal Processing},
  year={2018},
  volume={2018}
}
The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the proposed framework to the case of non-exactly sparse signals. The proposed method employs γ-sparsified random matrices and is based on a maximum likelihood (ML) approach, exploiting the property that the acquired measurements are distributed according to a… 
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