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.