The Analysis of Compressive Sensing Theory

Compressive sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Compressive sensing is a new type of sampling theory, which predicts that sparse signals and images can be reconstructed from what was previously believed to be incomplete information. As a main feature, efficient algorithms such as l1-minimization can be used for recovery. The theory has many potential applications in signal processing and imaging. This paper gives an introduction and overview on both theoretical and numerical aspects of compressive sensing and introduce the recent work on CS at present.