Reconstruction Algorithms in Compressive Sensing: An Overview

Abstract

The theory Compressive Sensing (CS) has provided a newacquisition strategy and recovery with good in the image processing area.This theory guarantees to recover a signal with high probability from areduced sampling rate below the Nyquist-Shannon limit. The problem ofrecovering the original signal from the samples consists in solving an optimizationproblem. This article presents an overview of reconstructionalgorithms for sparse signal recovery in CS, these algorithms may bebroadly divided into six types. We have provided a comprehensive surveyof the numerous reconstruction algorithms in CS aiming to achievecomputational efficiency