In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …

Recently, deep learning based image Compressed Sensing (CS) methods have been
proposed and demonstrated superior reconstruction quality with low computational …

The compressed sensing (CS) theory has been successfully applied to image compression
in the past few years as most image signals are sparse in a certain domain. Several CS …

Recently, deep network-based image compressed sensing methods achieved high
reconstruction quality and reduced computational overhead compared with traditional …

Gaussian random matrix (GRM) has been widely used to generate linear measurements in
compressive sensing (CS) of natural images. However, in practice, there actually exist two …

Image compressive sensing (CS) technology has attracted increasing attentions in the past
few years, and a great deal deep learning-based methods have been proposed. However …

Most conventional compressed sensing (CS) algorithms are impaired by the fact that the
optimization of image reconstruction suffers from the need for multiple iterative calculations …

We propose a general learning based framework for solving nonsmooth and nonconvex
image reconstruction problems. We model the regularization function as the composition of …

The progress achieved in transportation systems and artificial intelligence has amplified the
use of intelligent transportation systems and Autonomous Vehicles (AVs). Indeed, AV …

Compressive sensing (CS) is well-known for its unique functionalities of sensing,
compressing, and security (ie equal importance of CS measurements). However, there is a …