Fast and Efficient Compressive Sensing using Structurally Random Matrices

TL;DR

This paper presents Structurally Random Matrices (SRM), a fast, efficient, and theoretically sound sensing framework for large-scale compressive sensing that enables real-time processing with promising simulation results.

Contribution

The paper introduces SRM, a novel sensing matrix framework combining pre-randomization and fast transforms, improving efficiency and scalability in compressive sensing.

Findings

SRM achieves comparable sensing performance to fully random matrices.

SRM supports fast computation suitable for real-time applications.

Numerical simulations confirm the theoretical advantages of SRM.

Abstract

This paper introduces a new framework of fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed framework, we pre-randomize a sensing signal by scrambling its samples or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the transform coefficients as the final sensing measurements. SRM is highly relevant for large-scale, real-time compressive sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical sensing performance comparable with that of completely random sensing matrices. Numerical simulation results verify the validity of the theory as well as illustrate the promising potentials of the proposed sensing framework.