On some common compressive sensing recovery algorithms and applications - Review paper

TL;DR

This review paper discusses the fundamentals, algorithms, and practical applications of Compressive Sensing, highlighting its potential to reduce sampling requirements and expand signal processing capabilities.

Contribution

It provides a comprehensive overview of Compressive Sensing theory, algorithms, and real-world applications, summarizing recent developments and practical considerations.

Findings

Compressive Sensing enables accurate signal reconstruction with fewer samples.

Common algorithms for missing data reconstruction are reviewed.

Applications in real-world scenarios demonstrate the technique's versatility.

Abstract

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.