New Sensing Matrices Based On Orthogonal Hadamard Matrices For Compressive Sensing

Compressive sensing is a new methodology to reconstruct sparse signals from a few number of measurements. In this paper, we propose new sensing matrices for compressive sensing using orthogonal Hadamard matrix. The conventional sensing matrices based on orthogonal Hadamard matrix give acceptable performance but have some drawbacks. In contrast, the proposed sensing matrices are more suitable to compressive sensing compared with the conventional ones. Because they surpass their conventional drawbacks and give higher performance simultaneously. Extensive simulations on both synthesized signals and real images are conducted to show the power of the proposed sensing matrices compared with the conventional ones using convex optimization algorithms.