Low density frames for compressive sensing

We consider the compressive sensing of a sparse or compressible signal x ∈ ℝ^M. We explicitly construct a class of measurement matrices, referred to as the low density frames, and develop decoding algorithms that produce an accurate estimate x̂ even in the presence of additive noise. Low density frames are sparse matrices and have small storage requirements. Our decoding algorithms for these frames can be implemented in O(Mdvdc) complexity, where dc and dv are the row and column weight of the frame respectively. Simulation results are provided, demonstrating that our approach significantly outperforms state-of-the-art recovery algorithms for numerous cases of interest. ©2010 IEEE.

Duke Scholars

Author Vahid Tarokh Electrical and Computer Engineering