Measure What Should be Measured: Progress and Challenges in Compressive Sensing

TL;DR

This paper reviews recent progress, challenges, and future directions in compressive sensing, highlighting its theoretical foundations, emerging applications, and the ongoing debate about its practical impact.

Contribution

It provides a comprehensive overview of recent developments in compressive sensing, discusses unresolved theoretical questions, and explores future research opportunities.

Findings

Significant theoretical advancements have been made in compressive sensing.

Emerging applications like matrix completion are expanding its scope.

Key challenges include open theoretical questions and practical limitations.

Abstract

Is compressive sensing overrated? Or can it live up to our expectations? What will come after compressive sensing and sparsity? And what has Galileo Galilei got to do with it? Compressive sensing has taken the signal processing community by storm. A large corpus of research devoted to the theory and numerics of compressive sensing has been published in the last few years. Moreover, compressive sensing has inspired and initiated intriguing new research directions, such as matrix completion. Potential new applications emerge at a dazzling rate. Yet some important theoretical questions remain open, and seemingly obvious applications keep escaping the grip of compressive sensing. In this paper I discuss some of the recent progress in compressive sensing and point out key challenges and opportunities as the area of compressive sensing and sparse representations keeps evolving. I also attempt to assess the long-term impact of compressive sensing.