2016
Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices
ICASSP 2016accepted
In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters dep…