Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be
inferred from limited measurements of scattered electric fields. Parsimonious models provide …

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 …

Edited by the people who were forerunners in creating the field, together with contributions
from 34 leading international experts, this handbook provides the definitive reference on …

Fully updated throughout, with several new chapters, this second edition of Introduction to
Inverse Problems in Imaging guides advanced undergraduate and graduate students in …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

We propose simple and extremely efficient methods for solving the basis pursuit problem
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …

Compressive sensing is a signal acquisition framework based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable …

We address the problem of visual classification with multiple features and/or multiple
instances. Motivated by the recent success of multitask joint covariate selection, we …