Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …

The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

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 …

In this paper, we propose a novel sparse signal recovery algorithm called the trainable
iterative soft thresholding algorithm (TISTA). The proposed algorithm consists of two …

Compressive sensing (CS) is an emerging field based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable, sub …

Compressed-sensing reconstruction of still images and video sequences driven by
multihypothesis predictions is considered. Specifically, for still images, multiple predictions …

We consider the application of compressed sensing (CS) to the estimation of doubly
selective channels within pulse-shaping multicarrier systems (which include orthogonal …

A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …

Sparse representation with respect to an overcomplete dictionary is often used when
regularizing inverse problems in signal and image processing. In recent years, the …

A multiscale variant of the block compressed sensing with smoothed projected Landweber
reconstruction algorithm is proposed for the compressed sensing of images. In essence …