Fast Fourier-Based Generation of the Compression Matrix for Deterministic Compressed Sensing

TL;DR

This paper introduces a fast Fourier-based method for generating deterministic compression matrices in compressed sensing, aiming to improve execution time and facilitate comparison of reconstruction methods.

Contribution

The work proposes a novel, efficient Fourier-based approach for deterministic matrix generation in compressed sensing, enhancing speed and comparison capabilities.

Findings

Significant reduction in matrix generation time.

Effective comparison of reconstruction methods via GUI tools.

Improved accuracy in sparse signal reconstruction.

Abstract

The primary goal of this work is to review the importance of data compression and present a fast Fourier-based method for generating the deterministic compression matrix in the area of deterministic compressed sensing. The principle concepts of data compression such as general process of data compression, sparse signals, coherence matrix and Restricted Isometry Property (RIP) have been defined. We have introduced two methods of sparse data compression. The first method is formed by utilizing a stochastic matrix which is a common approach, and the second method is created by utilizing a deterministic matrix which is proposed more recently. The main goal of this work is to improve the execution time of the deterministic matrix generation. The execution time is related to the generation method of the deterministic matrix. Furthermore, we have implemented a software which makes it possible to compare different methods of reconstructing data compression. To make this comparison, it is necessary to draw and compare certain graphs, e.g. phase transition, the ratio of output signal to noise and input signal to noise, signal to noise output and also the ratio of percentage of accurate reconstructing and order of sparse signals for various reconstructing methods. To facilitate this process, the user would be able to draw his/her favorite graphs in GUI environment.