This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

This work attempts to provide a plausible theoretical framework that aims to interpret modern
deep (convolutional) networks from the principles of data compression and discriminative …

Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

Most compressive sensing (CS) reconstruction methods can be divided into two categories,
ie model-based methods and classical deep network methods. By unfolding the iterative …

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in
electrical engineering, applied mathematics, statistics and computer science. This book …

Compressed sensing (CS) comprises a set of relatively new techniques that exploit the
underlying structure of data sets allowing their reconstruction from compressed versions or …

To more efficiently address image compressed sensing (CS) problems, we present a novel
content-aware scalable network dubbed CASNet which collectively achieves adaptive …

The synthesis-based sparse representation model for signals has drawn considerable
interest in the past decade. Such a model assumes that the signal of interest can be …

To enhance the confidentiality and the robustness of double image encryption algorithms, a
novel double-image compression-encryption algorithm is proposed by combining co-sparse …