The emerging field of compressed sensing has potentially powerful implications for the
design of optical imaging devices. In particular, compressed sensing theory suggests that …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by reducing the sampling rate required to acquire and stably recover …

In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

We give the first computationally tractable and almost optimal solution to the problem of one‐
bit compressed sensing, showing how to accurately recover an s‐sparse vector\input …

Hyperspectral imaging is a powerful technology for remotely inferring the material properties
of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light …

Compressive sampling (CS) is a novel signal processing paradigm whereby the data
compression is performed simultaneously with the sampling, by measuring some linear …

This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in
the presence of impulsive measurement noise. Recently, robust data-fitting models, such as …

The recently emerged compressive sensing (CS) framework aims to acquire signals at
reduced sample rates compared to the classical Shannon-Nyquist rate. To date, the CS …

In compressive sensing (CS), the goal is to recover signals at reduced sample rate
compared to the classic Shannon-Nyquist rate. However, the classic CS theory assumes the …