Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

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 …

In this paper, we survey and compare different algorithms that, given an overcomplete
dictionary of elementary functions, solve the problem of simultaneous sparse signal …

The underlying representations of many real-world processes are often sparse. For
example, in disease diagnosis, even though humans have a large number of genes, only a …

This paper proposes efficient algorithms for group sparse optimization with mixed l 2, 1-
regularization, which arises from the reconstruction of group sparse signals in compressive …

In this paper, we study alternating direction methods for solving constrained total-variation
image restoration and reconstruction problems. Alternating direction methods can be …

The use of convex optimization for the recovery of sparse signals from incomplete or
compressed data is now common practice. Motivated by the success of basis pursuit in …

Background modeling and foreground detection are important steps in video processing
used to detect robustly moving objects in challenging environments. This requires effective …

We introduce a novel family of invariant, convex, and non-quadratic functionals that we
employ to derive regularized solutions of ill-posed linear inverse imaging problems. The …

This thoroughly updated new edition presents state of the art sparse and multiscale image
and signal processing. It covers linear multiscale geometric transforms, such as wavelet …