Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …

摘要在传统采样过程中, 为了避免信号失真, 采样频率不得低于信号最高频率的2 倍.
然而对于数字图像, 视频的获取, 依照香农(Shannon) 定理会导致海量采样数据 …

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 …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

The recently introduced theory of compressive sensing enables the recovery of sparse or
compressible signals from a small set of nonadaptive, linear measurements. If properly …

This article describes a very different approach to the decentralized compression of
networked data. Considering a particularly salient aspect of this struggle that revolves …

This paper introduces a new framework to construct fast and efficient sensing matrices for
practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed …

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm.
In essence, CS enables the recovery of high-dimensional sparse signals from relatively few …

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of
potential benefits ranging from high-temporal-rate observation of physiological processes to …