Optimal sensing matrix for compressed sensing

This paper presents a novel framework of fast and efficient compressed sampling based on the restricted isometry property (RIP). The proposed framework provides three features, i) It is universal with a variety of sparse signals, ii) The reconstruction errors can be minimized, iii) It has fast computation, that is, it needs few iterations. All currently existing methods don´t satisfy these desired features. Experimental results are presented to verify the validity as well as to illustrate the promising potential of the proposed framework.